{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 波士顿房价预测\n",
    "- 1.特征观察\n",
    "- 2.特征工程\n",
    "- 3.模型选择\n",
    "- 4.模型训练\n",
    "- 5.模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 引入需要用到的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.preprocessing import StandardScaler,minmax_scale,OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入数据并查看数据总体信息\n",
    "#### 经过观察  \n",
    "数值型特征有：CRIM,INDUS,NDX,RM,AGE,DIS,TAX,PTATTIO,B,LATAT  \n",
    "类别特征有：ZN,CHAS,RAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13) \n",
      "       CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTAATIO       B  LATAT  \n",
      "0     15.3  396.90   4.98  \n",
      "1     17.8  396.90   9.14  \n",
      "2     17.8  392.83   4.03  \n",
      "3     18.7  394.63   2.94  \n",
      "4     18.7  396.90   5.33  \n",
      "(506, 1) \n",
      "    MEDV\n",
      "0  24.0\n",
      "1  21.6\n",
      "2  34.7\n",
      "3  33.4\n",
      "4  36.2\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 13 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null float64\n",
      "TAX        506 non-null float64\n",
      "PTAATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LATAT      506 non-null float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 51.5 KB\n",
      "None\n",
      "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
      "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
      "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
      "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
      "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
      "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
      "\n",
      "              AGE         DIS         RAD         TAX     PTAATIO           B  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
      "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
      "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
      "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
      "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
      "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
      "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
      "\n",
      "            LATAT  \n",
      "count  506.000000  \n",
      "mean    12.653063  \n",
      "std      7.141062  \n",
      "min      1.730000  \n",
      "25%      6.950000  \n",
      "50%     11.360000  \n",
      "75%     16.955000  \n",
      "max     37.970000  \n",
      "             MEDV\n",
      "count  506.000000\n",
      "mean    22.532806\n",
      "std      9.197104\n",
      "min      5.000000\n",
      "25%     17.025000\n",
      "50%     21.200000\n",
      "75%     25.000000\n",
      "max     50.000000\n"
     ]
    }
   ],
   "source": [
    "X,y = load_boston(True)\n",
    "X = pd.DataFrame(X)\n",
    "y = pd.DataFrame(y)\n",
    "X.columns = ['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTAATIO','B','LATAT']\n",
    "y.columns = ['MEDV']\n",
    "print(X.shape,'\\n',X.head())\n",
    "print(y.shape,'\\n',y.head())\n",
    "print(X.info())\n",
    "print(X.describe())\n",
    "print(y.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用直方图观察预测值的分布\n",
    "#### 观察结果：  　　\n",
    "- 　数据整体偏移左边，可用１ｌｏｇ将数据进行处理   \n",
    "- 　在ｌａｂｅｌ值大于49时的值分布比较多，可能是数据集将大于５０的值均记录成了５０，可将该部分数据认为是噪声，进行删除  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6137482668>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_color_codes(palette='deep')\n",
    "sns.distplot(y,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除label噪声，并将label进行log1p，并画出新的图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(490, 13) (490, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6137bd2128>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = y['MEDV'] < 50\n",
    "X = X[index]\n",
    "y = y[index]\n",
    "sns.distplot(y,bins=50)\n",
    "plt.show()\n",
    "y = np.log1p(y)\n",
    "print(X.shape,y.shape)\n",
    "sns.distplot(y,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用直方图查看连续型特征的分布情况\n",
    "其中CRIM和B和ZN两个特征的分布非常广泛，所以使用提琴图再进行观察,发现确实有疑似噪声数据，后面单独查看这两个特征与label的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LYwtYBAGXb+XjaGo2Dl7JwoE1BxHW8yHMHeaDrmrXBt6a5i+rwIQDV+4gu8CE4lIz5DIJOnq0Qg8vV3640gNh6IvEjZwivBl3Csnnb6Fzu1b49JlHEdxViRvGohq7jGpDKpHAT+kCP6ULcotKkZZVgG9P3sQO3Q2MC/LCwhB/htMDOHMjF//z03nsPn8LZkFAC5kEjnIZSswWHLySha+O6DG8uwozhnSG2tWx0rw8W4vuhaEvArvOGLB01wWUWQS8OSoAUwZ0rPWe/f1wbdkCc4b5YtGIrojZcxnrf0nFDt1NzBvmi5lDO9f7+pojU5kFK5Mv4ZPkS3Bt2QJ/66tBO2cHqH4PdrNFwI2cImTmFSP+xA0knc3E413a4fEuSkgl5QfVebYW3QtDvxkrNVsQf+IGjqVl4+EO7lj6t97w9mjV4Ot1d1LgjZEBmDqgIxYnnMX/7rqA+BPX8eITfg2+7qYsq8CEZ9cfwQl9DsYFeSHqyW4oMFU+eC+TSqBp44TRvR+CTztn7PwtA4lnM3E9uwgTHtXAsYXMhltATUGtdvdSUlIQGhqKkJAQrFmzpsrrJpMJL774IkJCQjBhwgSkp6cDANLT09GzZ0+MGTMGY8aMQVRUVP1WTzXKKTRhTcoVHEvLxrQBHfH1zMcaJfD/zNOtJVb+42Gsn9YHJrMFL3xxAtt+TUdhDaedill6diEiV+3H2Zu5+GTSw1j6t95o3eree+vuTgr87VENwnt64rwhD6v2XEZuUWkjVUxNldU9fbPZjOjoaMTGxkKlUiEyMhJarRa+vr4VbbZs2QJXV1fs2rULCQkJ+Oijj7Bs2TIAQIcOHRAfH99wW0BVXM8pwmf7U1FqtuDp/t6YMsAbGbnF1bat6/n9tfF4FyV+enEoFn9/Bp8fSsM5Qx4iH/ZCFx7oBQBcu1OAiasPoMhkxsbn+qFvpza1nlcikeAxn7ZQujpi48Fr+O++qxjg68HjKFQjq3v6Op0O3t7e0Gg0UCgUCAsLQ1JSUqU2P//8M8aNGwcACA0NxYEDByAIQsNUTPd0KTMfn+69ArlUgllDfRDg6YqiUgtSLtyu9mEyN87/U0uFDLOG+mDeMD+4OMjx2YFr+PbkdZSaLY2yfnt1K68Ez6w7jJIyC76e9dh9Bf6f+bRzxpTHOiKnyIQXvzyBrAJTPVdKzYXV0DcYDFCr1RXPVSoVDAZDlTaenp4AALlcDhcXF2RnZwMo7+IZO3YsJk+ejKNHj9Zn7fQXp68b8dn+VLRxUmDWUJ+Kg3/2RO3miDmP+2CQb1scvJKFlcmXcCOnyNZl2URecSmmxh5GZm4JYqf2qfMprp3atsIzj3VEek4Rpn92BMWN8C2Omh6roV/dHrtEIqlVG6VSieTkZHzzzTd4/fXX8fLLLyM/P78O5VJN9ly4hS+PpMGrdUvMGNwZrr9fYGWP5DIpRvXwxLSBHVFcakbM7sv46oheVN8OLRYB8784jvMZefhk8sMI6tC6Xpbr084Zb4UF4FhaDl7bphPVe0q1YzX01Wo1MjIyKp4bDAYolcoqbW7eLL80v6ysDHl5eXB3d4dCoUDr1uW/zIGBgejQoQOuXr1an/UTgJP6HCzZeQ4d2rTCtIEd0VLRNM7g8FO6YIHWD13ULljx8yXM2XwMecXiOBC5LPECks/fwtuju2NYF6X1Ge7DsK5KvDLcH/EnbmDFz5fqddnU9FkN/R49eiA1NRV6vR4mkwkJCQnQarWV2mi1WsTFxQEAfvzxR/Tv3x8SiQRZWVkwm8u/Yur1eqSmpkKj0TTAZojXbzeM2PKrHt0fcsPUAR3hIG8agX+Xk4Mck/p1wNxhPvjpjAGjV/6Ccxm5ti6rQf30WwY+/vkSJjzSHpP7dWiQdcwd5otxQV74310XkHjGYH0GEg2roS+XyxEVFYXp06dj1KhRGDlyJPz8/LB8+fKKA7qRkZHIyclBSEgIYmNj8corrwAAjhw5gtGjR2P06NGYP38+3nnnHbi73//AYFS9C4Y8fHlYDy/3lnhndPcGueCqMUgkEjzVtwO+mNEfBSVlGPufX7Dt13Rbl9UgLt/Kx8KvT6Jnezf8e2xgla7S+iKRSPBeRA8Eernipa9PIPV2QYOsh5qeWl2cNXToUAwdOrTStAULFlT87ODggI8//rjKfKGhoQgNDa1jiVSdtDsF2HzoGpSuDpg6oFOT6dK5l76d2mDH/EGY/8VxvLzlJI5ey8bb4d1sXVa9yS8pw8yNv0IhlyJm8iMNfiGVYwsZYiY9gvCV+zBz46+ImzsATgpejyl2TXPXUORu55Vgw8FrcHFsgWkDm0fg36V0ccSm5/ph9uM++OJwGiJX7W8WZ/cIgoBXvj6Jq7cLsPIfQfByb9ko69W0ccLHfw/Chcw8vLbtFA/sEkO/qckrLkXs/vKD4dMGdISzQ/Pbc5PLpHhtRFd8+syjuHanEM99dhTnM/JsXVadfLL7Mnb+loE3RnbFAJ+2jbruIf7t8MrwLvju5A2s+yW1UddN9oeh34QUmczYcOAa8kvKMOWxjvBwdrB1SQ0qpJsKCS8MhtrVERsOpCLprAGWJrinmnw+Ex/9dB6jez2E5wZ1skkNcx73wfBuKrz7/VkcvHLHJjWQfWDoNxFlZgve/vY33Mgpwt/7dICmjX1eZl9mtiA9u7Dax4MM+dDBwwkxkx9GUAd3JJ3LxIYDqSg0NZ2xe1JvF2DBF8fRVe2KD8b3bLADt9ZIJBL8z8Re8G7jhHmfH0OGsfphOaj5Y+g3AYIg4K3409h/+Q5G934IAZ72O2ZNQwz54NhChvEPt8eY3g/hcmYB/tNEruIt+P3ArVQqwZqnH7H5sRcXxxZY/fQjKDSZMXvzrygp4xW7YsTQbwL+k3wJXxzW4+n+HdCvk4ety7EJiUSCfp088PyQzrAIwKo9l/HrtSxbl1UjQRCwaKsOFzPzsOKpILv5ZuancsFHE3rheFoO/r3jjK3LIRtg6Nu5rb+m46OfLiAiyAvPD+GNSDRtnDB3mC+8PZyw7dh1fLDznF2OMbNqzxUknLqJ10Z0xWC/drYup5JRPTwxc0hnbDqYhq1/uh7CWGiqsWvOWMgB3JqL5nfqRzOy9+ItvL5Nh4G+Hnh/fE9k5rEfFgCcHeSYNrATdp0x4LuTN3EhIx8r/hEEf5WLrUsDAOw8nYEPfzyHsJ6edvtB/WpoF5y6bsSbcafQVe2CQC835JWU1XjrTN6Nq/ngnr6d+u2GEbM3HYOv0hkxkx9pslfbNhSpRILQ7mr8z4SeuFNQgtEr9+HzQ2k2Pw/90JU7mP/lcfRs744PI2134NYauUyKFU8FwaOVAjM3/opsDsUsGkwSO3Q9pwjTYo/AxVGO9dP6wtXRfkfMtLV+nT3w/YLB6NOxDf4ZdwpzNh+DsdA2g7advZmL6RuOon3rllg3tY/dX/3q4eyAmMmP4FZeCeZ/eRxmS9M7HZbuH0PfzmQVmDBl3WEUlZqxflpfqN3sb0x8e6N0ccRn0/rijZFdseuMASOWpyD5fGaj1qBLz8FTnx5EK4UcG57tizZWbnVoL3pp3BE9pjv2XryNtfs4Aq4YMPTtSF5xKZ7+7yGkZRXivYgeaOUgq/N57mIhlUowc6gPts8ZUN7nH3sEr245iZxGOAD567UsTPr0EFop5PhqZv8md6vCv/ftgKf6arDxwDWcudG8Rzglhr7dKC41Y8aGoziXkYe/99Egt6jMZrc2bMp6tnfHdy8MwpzHfbD9+HUM+2g3vjqSBksDdV1s/TUd//j0ENq6OGDLrMa/+Xx9+X+juyPA0wVfH9VDn1Vo63KoATH07UCZ2YJ5nx/HoatZeDMsoM63zRM7xxYyLBrRFTteGARfpTNe23YKY/7zC1Iu3Kq3A73FpWa89c1pvLLlJII6uGPLrMfwUCMNotYQHOQyvBfRA86Ocqzfn8ordpsxhr6NWSwCFm3TIfGsAe+M7o7h3VS2LqnZCPB0xdczH8PSv/VCVoEJz6w7jL+vOYjkc5l12vNPPp+J0GUp2HjwGp4f0hmbnuuHts1gHKS2zg54dmAntJBJsO6Xq7iVV2LrkqgBMPRtyGIREPXtaWw/dh0vh/jjmcc62rqkZkcikWBcUHv8/MpQvB3eDdfuFGLa+iMIWboHq/ZcrnVXRpnZgp9+y8DktYcwLfYI5FIJNk/vh3+OCoBc1nz+jNq0UuDZgZ0gAFiTcrlJDHdB98e+zylrxiwWAf+MO4Uvj+gxc2hnzNP62rqkZs1BLsO0gZ0wub83vj91E+v3p+L9H87h/R/OoavaBQ97t0av9m5QuTrCQSZFXkkZ8opLccNYDF26EcfSsnEn3wSVqwPeHBWAKQM6NttrJ5Sujpg5uDP++8tVrN13BVMe6wigcYeDpobD0LeBMrMFr207hW3H0jFvmC9eHu5vtxfxNDctZFKM6e2FMb29oM8qxA7dTey/fBvfnbyBzw+lVTuPi6McHT1aYUR3NWYM6YSOHs6NXHXja+vigJlDOuO/+67iv/uuoq2LA2YMts+ri+n+MPQbWaGpDPM+P46fz2XipSf8seAJP1uXZBfuDslcnYY6VVXTxgmzH/fB7Md9YLEI0GcX4nZ+Cc5n5OFcRh5atpDB1bEF3J1aVHwoy6XNc+++Ou5OCswe6oPPj6RhccJZGIzFeG1kV7RoRt1ZYsTQb0S380sw/bOj0KXn4P+PDcTk/t62LsluFJVacPxy9aNmBnVwb/D1S6USeHu0grdHK6hcHQHwmxcAODnIMW1AJ5xMz8bafVdx6GoWlv6tN3yVdf+2Yyw0Ia+k+nsjuDjIOdZPA2HoNxJdeg5mbfwVdwpMiJn8CEK7q21dElGtyKQSvPiEP54IUOGN7acQ9vFeLHjCD88N6gQH+YPfI4ADvNkGQ7+BCYKALUfT8a/402jn7IBtswcg0MvN1mWJ3r32Mnnlc/VGBHriYe/WeDPuNJbsPI+vjujxxsiuGN5NDamU34yaCoZ+A8ouMOHNb07h+1MZGODjUT6qYTM4n7s5uNdeZmN0JzVVShdHfPrMo9hz4Rb+veMMZm06hi4qF8wZ5oORgZ7N9oym5oSh3wAEQUDCqZuI/u4MsgtNeH1kV8wY3Bky7g01Ku7NN5yh/u0wcMFgfKe7gU+SL2PBlycQ3eoMxgV5YWyQF7o/5NpgZ6TxWEDdMPTr2fmMPPx7xxnsu3Qb3R9yxbqpfdid04CsnfVz6Gp2ta9xb77u5DIpxgW1x5heXthz4Ra+OqLH+v2pWLvvKh5yc4Q2QIl+nTzQp2Obeh0tlscC6oahX08uZeZhedIl7NDdgLODHNFjumNSP2/u3TcwW5/1Q+VnPg3rqsSwrkrcyS9B0rlMJJ4xYPux69h0sPzah7bOCvi0c4av0hl+Smf4KJ0hAVBkMsOxhZTXqTQihn4dlJotSD6XiY0Hr2HvxdtwUsgwe6gPZgzujNZNZDx1uj/3+mbBroXyG7NMfFSDiY9qUGa24LcbuTh6LRsXMvJwMTMP3528gdziyl0zcqkEzo5yuDjI4aSQw0khwwl9NjStneDeSoHWTi3Q2kkB99//tcd7IjcltQr9lJQULF68GBaLBRMmTMDzzz9f6XWTyYRFixbht99+g7u7O5YuXYr27dsDAFavXo2tW7dCKpXiX//6FwYPHlz/W9GICk1lOHQ1Cz/9loGdpzOQXVgKTzdHvBzij3/068ADtc3cvb5ZsGuhMrlMil4ad/TS/PGNSxAE3MovwZVbBTh7MxdHrmb9PuRFGfKLy4e+MOQW41xGHoruEe5yqQROClnFh8Tdn89m5MK7jRNaOynQ1sUB7Zwd0M7FAW1aKfit+3dWQ99sNiM6OhqxsbFQqVSIjIyEVquFr+8fY8Vs2bIFrq6u2LVrFxISEvDRRx9h2bJluHTpEhISEpCQkACDwYBp06bhxx9/hEz24Of2NqYyswVpWYU4l5GHk+k5OJ6WgxNpOTCZLWilkOGJbio82fMhDOvSrlkNukXUUCQSCZQujlC6OKJ965Y1nuc/xL8t2rk4IKewFDmFpcguNCGn0ITswlKk3inAmeu5KDSZUWgqQ6HJDENuCQpNBfj1WjbM1QyfLZWUfwtp5+xQ6cOg4vGn566O8mbd3WSyYogCAAAKIUlEQVQ19HU6Hby9vaHRaAAAYWFhSEpKqhT6P//8M+bNmwcACA0NRXR0NARBQFJSEsLCwqBQKKDRaODt7Q2dToegoKAG2pzygczKLALMFgGlFgvM5j+el1ksKDMLMJktyC8pQ8Hvj/wSM3IKTTDkFiMzrwSG3GIYckuQnl2I0t9vXKKQSRHg6YJpAztioG9b9O3UBo4tmsaHF1FT5CCXQeUq+/0K6T+kZxfWeCB3kJ8H3FoqkF1gwu38EtzKK8Gtu//+/ridX4JLhjzcyi+p+Pv+M4VcWv7h4KyAi2MLuDjK4Xr335bl/zo7yOHYQgYHuRQOd/+VS6GQS+EgvztdihZSKaQSCSRSQCqRQCop/1ciufu8fFpjfshYDX2DwQC1+o+rR1UqFXQ6XZU2np6e5QuUy+Hi4oLs7GwYDAb06tWr0rwGg6G+aq/i8Q+TkXrnwe/64yCXQuXqCJWrA7p5umJEoBq+7Zzhp3JGF7VLna4+JKKGJ5VI4NayBdxatkDHtve+i5kgCDAWlf7xgfDnD4f8EtzONyGvuBQZucXIKy5FblHZPbuc6koiAZ4b2An/erJbg60DqEXoV3enob9+KtXUpjbz/tX169cRERFhraxqtfn9UVdmADd/fxyqh+XVl61N4DV7qcPeXqtvjV1HY25bXdZX33VKALj+/vCq52VX59h5IGLdg817/fr1WrWzGvpqtRoZGRkVzw0GA5RKZZU2N2/ehFqtRllZGfLy8uDu7l6ref/q0CF7ilkioubF6tHHHj16IDU1FXq9HiaTCQkJCdBqtZXaaLVaxMXFAQB+/PFH9O/fHxKJBFqtFgkJCTCZTNDr9UhNTUXPnj0bZkuIiMgqq3v6crkcUVFRmD59OsxmM8aPHw8/Pz8sX74cgYGBCA4ORmRkJF599VWEhITAzc0NS5cuBQD4+flh5MiRGDVqFGQyGaKioprMmTtERM2RRKiu452IiJolnlxORCQiDH0iIhERxdg7K1aswNdff402bcpP6Fy4cCGGDh1qs3qsDWthK1qtFq1atYJUKoVMJsP27dttVssbb7yB3bt3w8PDAzt27AAA5OTk4KWXXsL169fh5eWFZcuWwc2tcUcwra4ue/n9unnzJhYtWoTbt29DKpVi4sSJmDJlis3ft5rqsof3raSkBJMmTYLJZILZbEZoaCjmz58PvV6PhQsXwmg0olu3bliyZAkUisYbYqOmul5//XUcPnwYLi4uAID3338fAQEB97dwQQQ+/vhjYe3atbYuQxAEQSgrKxOCg4OFtLQ0oaSkRAgPDxcuXrxo67IEQRCEYcOGCXfu3LF1GYIgCMLhw4eF06dPC2FhYRXTPvjgA2H16tWCIAjC6tWrhSVLlthFXfby+2UwGITTp08LgiAIeXl5wvDhw4WLFy/a/H2rqS57eN8sFouQn58vCIIgmEwmITIyUjh+/Lgwf/58YceOHYIgCMJbb70lbN682S7qeu2114QffvihTstm904j+/OwFgqFomJYC6qsT58+VfZGk5KSMHbsWADA2LFjkZiYaBd12QulUonu3bsDAJydndG5c2cYDAabv2811WUPJBIJWrUqv3K3rKwMZWVlkEgkOHjwIEJDQwEA48aNa/S/0Zrqqg+iCf3NmzcjPDwcb7zxBoxGo83qqG5YC3v5AwCA5557DhEREfjqq69sXUoVd+7cqbi4T6lUIiur+tEubcFefr/uSk9Px9mzZ9GrVy+7et/+XBdgH++b2WzGmDFjMGDAAAwYMAAajQaurq6Qy8t7v9VqtU3+Rv9a1933bOnSpQgPD8e7774Lk8l038ttNqE/depUPPnkk1UeiYmJeOqpp7Br1y7Ex8dDqVTi/ffft1mdwgMMTdFYvvjiC8TFxeHTTz/F5s2bceTIEVuX1CTY0+8XABQUFGD+/Pn45z//CWdnZ5vW8md/rcte3jeZTIb4+Hjs2bMHOp0OV65cqdLGFn+jf63rwoULWLhwIXbu3Ilt27bBaDRizZo1973cZnMgd/369bVqN2HCBMyaNathi7mHBxmaorGoVCoAgIeHB0JCQqDT6dCnTx8bV/UHDw8PZGZmQqlUIjMzs+IAoK21bdu24mdb/36VlpZi/vz5CA8Px/DhwwHYx/tWXV329L4BgKurK/r164cTJ04gNzcXZWVlkMvlyMjIsOnf6N269u7di+eeew4AoFAoEBERgXXr7n+gnmazp38vmZmZFT8nJibCz8/PZrXUZlgLWygsLER+fn7Fz7/88otN36fqaLVafPPNNwCAb775BsHBwTauqJy9/H4JgoA333wTnTt3xrRp0yqm2/p9q6kue3jfsrKykJubCwAoLi7G/v374ePjg379+uHHH38EAMTFxTX632h1dXXu3LniPRME4YHfM1Fckfvqq6/i3LlzAAAvLy9ER0fb9JN7z549ePfddyuGtZg9e7bNarlLr9dj7ty5AMr7Ep988kmb1rVw4UIcPnwY2dnZ8PDwwAsvvIAnnngCL774Im7evAlPT08sX74c7u6Nex/c6uo6fPiwXfx+HT16FJMmTYK/vz+kUmlFvT179rTp+1ZTXTt27LD5+3bu3Dm8/vrrMJvNEAQBI0aMwLx586DX6/HSSy/BaDQiICAAH330UaOesllTXc888wyys7MhCAK6du2Kd955p+KAb22JIvSJiKicKLp3iIioHEOfiEhEGPpERCLC0CciEhGGPhGRiDD0SXSCgoIAlA8J0KVLF2zcuLHitejo6IrRRV9//XVotVqMHj0aoaGhWLRoUaXL8e8u567t27cjOjoaAHDlyhU8/fTTGDNmDEaOHIm33nqroTeLqFYY+iRqHh4e2LBhQ41jmCxatAjffvstdu7ciW7duuGZZ56p1XgnixcvxpQpUxAfH48ffvgBkydPru/SiR4IQ59ErU2bNnjssccqrlitiUQiwdSpU9G2bVukpKRYXW5mZmalgfW6dOlS51qJ6gNDn0RvxowZWLduHcxms9W23bp1q3ZArr+aOnUqpkyZgunTp2P9+vUVl9QT2RpDn0RPo9GgZ8+e+O6776y2tXYB+93RGMePH4/vv/8eI0aMwKFDhzBx4sQHGgaXqL4x9IkAzJw5E2vXroXFYrlnu7Nnz8LHxwcA4ODgUCnIjUYjWrduXfFcpVIhMjISMTExkMvluHDhQsMUT3QfGPpEAHx8fODj44Pdu3dX+7ogCNiwYQNu3bqFwYMHAwD69u2Lb7/9FkD5SIg//PAD+vXrB6D8PsilpaUAgFu3biEnJ6di6GoiW2o24+kT1dXs2bMrbit415IlS/DJJ5+guLgYvXr1woYNGypGW3zzzTcRFRWFjRs3QhAEjB07tuL+A7/88gsWL14MBwcHAOUjvbZr165xN4ioGhxlk4hIRNi9Q0QkIgx9IiIRYegTEYkIQ5+ISEQY+kREIsLQJyISEYY+EZGIMPSJiETk/wAzOLpZn5kLMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pKT6EwEE6BaoTQvSEtPQVdqK0BocDt2zpAyycEEHwYB3//KaEFptd6XKEENdIQl9hl27ijo1wz9DXatTcMj6Cslore06XKV2OEOIaSegrLLekmgAfraJz7jgzyujPuIgAdp08T6X04QvRr0noKyy3uJqxEQF9tiZuT6UlhgOwPadE4UqEENdCQl9BNruDE6XVjHHT/vzL6QfpmD3awPGSao4WWZQuRwjRQxL6Csovr6Ox2e62N3G/a8bIECL0Pmw9UkxdU4vS5QghekBCX0HufhP3uzRqFemTo2m02tiWU6x0OUKIHpDQV1BuSesc+u4y0Vp3GAN9uGl0GDmFFunmEaIfktBXUOt0yv59vhD6tUqODyVS78vmw0WUVjcqXY4Q4ip0K22ys7NJTU1lzpw5rF+/vsP+gwcPsnjxYsaOHctHH33Ubt+YMWNYuHAhCxcu5P7773dN1QNEbol7Tr/gjEat4gdTorE5HPzhf4+RX14rUzQI0U84nYbBZrORkZHBhg0bMBgMpKenk5KSwsiRI9uOCQ8P56mnnuL111/vcL6Pjw9bt251bdUDwIWaJi7UNDEmvP907Vwu2M+bRRMjeO/LQjK2Hef7YwwdjpEpGoRwP05DPycnh9jYWKKjowFIS0sjKyurXehHRUUBoFb3r24KJR0v6V83cTszMXoI5XVWdh4/z/DQwQwPcZ+poYUQnXOa0mazGaPR2PbaYDBgNpu7fYGmpiaWLFnCbbfdxo4dO3pW5QCUeyn0+2H3zuUeuHEkQYN1vHfQRL0M4xTC7Tlt6Tscjg7brubp0V27dmEwGDCZTCxfvpz4+HhiYmKursoBKLe4mki9L/p+3v3hq9Pwg+tjWPdZHpsOFfKjabFu/3SxEJ7MaUvfaDRSWlra9tpsNhMWFtbtCxgMrX290dHRXH/99eTm5vagzIEnt6R/PInbHZF6X1ITjBwvrWFvXrnS5QghrsBp6CcmJpKfn4/JZMJqtZKZmUlKSkq33txisWC1to7gqKio4NChQ+3uBXiqxmYbZy7U9uv+/O+aMSKYMeEBfHi0hIJyWVdXCHflNPS1Wi2rVq1ixYoVzJ8/n3nz5hEXF8fq1avJysoCWm/2JiUl8dFHH/HEE0+QlpYGQF5eHkuXLuXWW29l+fLl3HvvvRL6wMnSGuwOGNtPR+50RqVSkT45ikBfL/520CTTNAjhprq1clZycjLJycnttq1cubLt+/Hjx5Odnd3hvMmTJ7Nt27ZrLHHg+fdN3ECFK3EtX52GH06N5eV/5fHelybmjus4jFMIoSwZY6mA3OJq/L21RA1x3zn0eypS78st4yP49nwtb+3LV7ocIcR3SOgr4NJNXLV6YI5ymTJ0CJOi9by+J5/d315QuhwhxGUk9PuQpd5KQUUducXVxAT7dpi2oKnZpnSJLqFSqVg4MZKhIYNZ+fevKbE0KF2SEOIiCf0+VNPUwpbDxTQ027DbIftUWbsvq63jMxH9lU6r5slFCTQ123jgnUM0y6LqQrgFCf0+VmJpnZUyPHDg9ed/V0zwIP7f0vEcKqji/314QulyhBB0c/SOcJ0SSwNqFYQFeCtdSp9YMCGCr85V8tqes1wXO4R5F9favVaWeis1XQwL9ffWykRvQnRBQr+PlVQ1EurvjZfGc37J+u38MXxtquKx93MYHR7AsJDB1/yeNU0tZJ8q63SfzO4pRNc8J3ncRImlwSO6di6n06r5y52T0WhU/Oztr2gcIDesheiPJPT7UGW9lerGFsIDfZQupc9F6n15/vaJnCitYdXWo0qXI4THku6dPnT6fC3gGTdxAVpsdgor69tex4X5sXx6LG/uO8fwUD/umBIt3TBC9DEJ/T7079D3jJZ+Q7Odw3kV7bbFGfwZHjqY5z4+ySijPzeN6v6MrUKIayfdO33o9PlaAny0DPb23J+1apWKH0yJYZBOw++3HKW6sVnpkoTwKBL6fehbc61bdu1c6obp7Ks3nhL289bygykxlFQ18tjGnE4X6hFC9A7PbXL2scZmG+fK65kVH6J0KR101g1zyaQYfa9cc2jIYO6/cTh/2ZXH65/n85OZw3rlOkKI9qSl30e+NddiczjcsqWvlB9MiWbuWANP/fM4X+Z3/kNHCOFaEvp95PjFOfQjPOQmbneoVCqeXTaByCG+3P/2IYqrZGI2IXqbhH4fyS2pxlenYchgGaJ4SYvNTk1jM/+1KIEGaws/fv0Ap8zVFFbWY6m3Kl2eEAOShH4fyS2uZmToYNSqgTmHfk80NNvJPlWGqaKBZddFk3e+ll/9/QifnbzQ5bw6QohrI6HfB+x2B7kl1YwMGzhr4rpavMGftPHh5JZUsz2nWEb0CNFLZPROHyisbKC2qYW4MD+lS3FrN4wIwdLQzO5vy3hz3zl+lza2z2u40uydIDN4iv5PQr8P5JZYAIgz+HGhRvqqryR1nJHaxhZe3X2W2ODB3DUttk+vf6XZO0Fm8BT9n4R+H8gtqUGtguEhgyX0nVCrVCyZHMUg79YndtUquHNq3wa/EAOZ9On3gdziaoaH+uHtpVG6lH5Bo1bxx4UJpIwO4/HNR3n7i3NKlyTEgNGt0M/OziY1NZU5c+awfv36DvsPHjzI4sWLGTt2LB999FG7fZs3b2bu3LnMnTuXzZs3u6bqfuZYsYWEiACly+hXdFo1a380mZTRYfxuy1FeyvpWbu4K4QJOQ99ms5GRkcGrr75KZmYm27dv5/Tp0+2OCQ8P56mnnuKWW25pt72qqoo1a9bw3nvvsXHjRtasWYPFYnHtJ3BzZbVNlFgaSYgMVLqUfsdbq2Hdj77H4kmR/Penp/jdlqO0yALrQlwTp6Gfk5NDbGws0dHR6HQ60tLSyMrKandMVFQUo0ePRq1u/3Z79uxhxowZ6PV6AgMDmTFjBrt373btJ3BzR4taf8iNi5DQ7wmdVs1/L5vAT5OH887+ApZvOEBFndwXEaKnnIa+2WzGaDS2vTYYDJjN5m69+bWcO1AcK26dfmFcpHTv9JRareI388bwzNLxHMyvZMFLezhRWqN0WUL0S05Dv7N+VFU3nyq9lnMHiqNFFoYGDyLAx0vpUvq926ZEs/Gn03E4HNz/16/47OR57NLPL8RVcRr6RqOR0tLSttdms5mwsO6tdnQt5w4U3xRZGCf9+S4zIVrPP1fOYlZcCJ/kmnkl+wznqxuVLkuIfsNp6CcmJpKfn4/JZMJqtZKZmUlKSkq33nzmzJns2bMHi8WCxWJhz549zJw585qL7i+q6q0UVjaQKKHvUvpBOjIWjmPZ96I4X9PESztP82luKc1yk1cIp5w+nKXValm1ahUrVqzAZrOxdOlS4uLiWL16NQkJCcyePZucnBx+8YtfUF1dza5du3jppZfIzMxEr9fz85//nPT0dAAeeOAB9PreWZTDHV3qz0+Qm7gup1KpmBQzhDiDP//8poRdJy/w1blKvj/GwA0jg5QuTwi31a0ncpOTk0lOTm63beXKlW3fjx8/nuzs7E7PTU9Pbwt9T/NN28gduYnbW/y8tdx2XTRThgbx0dESPjhcxKGCSn47fwxzxho87h6SEM7IE7m96GiRhUi9r8yh3weGhQzm/uQR/PD6GGwOB/f99SvS1+1jX1650qUJ4VYk9HvRseJq6c/vQyqVioTIQP76k+v50+JETBX13PHKF/xg/T72n5HwFwIk9HtNTWMzZ8vqSJDx+X1Oq1bzw6kxZD92E6tuGUvehTpuX/8FP3zlCw7KWrzCw0no95JvClv78xOjPOfGtbvx8dJwz8xh7H7sJn6XNoZT5hqWrdvHXa/t56tzlUqXJ4QiJPR7yWFTFQATJfQV5+OlYcWs4ex+LIXH548ht7iapWv38uPXD3C4QMJfeBYJ/V5yuKCK4aGDCRwkT+K6C1+dhnuThrP7P2/i1/NG801hFYv/Zy8r3jxIflmd0uUJ0Sck9HuBw+Hga1MVE6Olle+OBum03J88gj3/mcKjqaPYl1fO3OezefqjE9RbZUF2MbBJ6PeCwsoGymqbmCSh79YGe2t54KaR7HrkRm6ZEM7az/L44Sv7+dpUJXP3iwFLQr8XfH2xP39SzBCFKxHdERbgw59vm8imn91AsJ83731p4rXPz1IpUziLAUjWyO0FX5uq8NaqGWX0V7oU0QlLvZWapo7dOIYAb9bcMZE1u/L48Ggpq3d+y/yEcKYMHdKtJ3u7et9L/L21sqi6UJyEfi84XFBJYmQgXhr5Rcod1TS1kH2qrNN9k2L0TB0WTLzBn02HCtnydRHHii0smRxFoO+Vb8pf6X0BkuJDJPSF4iSVXMzaYudocTWTYqQ/vz8bMkjHPTOGceuECPLL61iddYojF7vthOjPpKXvYidKq7G22JkYLf3516LFZqewsr7L/U3Ntl6vQa1SMW14MHFhfmz8qpB/fGmivrmFZ5ZOwFencem1pGtI9BUJfRc7XHDxoSxp6V+ThmY7h/O6njKhL3+TCvbz5t5Zw9lx3My2IyWcKq1lzQ8nEWdw3T0b6RoSfUW6d1zsy3OVGAK8iQj0UboU4UIatYrUcUb+e9l4ymqbuHXN52z80qR0WUJcNQl9F3I4HOw/U87UYcEyj/sANXV4MB+unMXEaD2Pvp/Dw+8dkQe6RL8ioe9C+eX1nK9pYupwWblpIAsL8OHtFVP51ffj+OBwIYv+8jmnz9coXZYQ3SKh70KX5myfOixY4UpEb9OoVfzq+/G8dc/1lNdauXXN53ySa1a6LCGcktB3of1nKwjx0zEidLDSpYg+MisulMwHZzEuIoCMbblsOVwkC7QLtyah70IHzlZw/bAg6c/3MMZAH/527zTunBrDgfwK1v0rj/LaJqXLEqJTEvouYqqop6iqQbp2PJRWo+ZnN47gx9NiqapvZs2u0xwtsihdlhAdSOi7yP6zrWPK5SauZxsdHsAvUkYS6u/NuwcK2J5TTItdunuE+5DQd5H9Z8rRD/IiPkwmWfN0QwbpuC9pODeMCGZvXjnrs89QWS8zdgr30K0ncrOzs3nyySex2+0sW7aM++67r91+q9XKY489xrFjx9Dr9Tz//PNERUVRWFjI/PnzGTZsGAATJkwgIyPD9Z/CDew/W8H1Q4NQq6U/X2nuMIWDVq3mlvERxAYP5oNDhazZeZoQPx23T4np9WsLcSVOQ99ms5GRkcGGDRswGAykp6eTkpLCyJEj247ZuHEjAQEBfPrpp2RmZvLcc8/xwgsvABATE8PWrVt77xO4gbNldRRU1POTmcOULkXgXlM4JEYGEhHow7sHCvjPTd9wylzLYzePwlvr2rl7hOgup907OTk5xMbGEh0djU6nIy0tjaysrHbH7Ny5k8WLFwOQmprKvn37PGrloc9OngfgplFhClci3FGwnzf3J49gyeRIXttzliX/s5e8C7VKlyU8lNPQN5vNGI3GttcGgwGz2dzhmPDwcAC0Wi3+/v5UVlYCUFhYyKJFi/jRj37El19+6cra3caukxcYHjqYmOBBSpci3JSXRs1Dc+J55cfXUVzVwC0v7uHvBwo8qnEk3IPT0O/sL+V3x6F3dUxYWBi7du1iy5Yt/PrXv+bhhx+mtnZgtXDqrS18caZcWvmiW+aMNfDhyiQmxej59Qff8MC7h7DUNytdlvAgTkPfaDRSWlra9tpsNhMWFtbhmJKSEgBaWlqoqalBr9ej0+kYMqR1XvmEhARiYmI4e/asK+tX3L68cqwtdgl90W3GQB/e/slU/vPm0XxyzMz8F3dzpFAWaBF9w2noJyYmkp+fj8lkwmq1kpmZSUpKSrtjUlJS2Lx5MwAff/wx06ZNQ6VSUVFRgc3WOlLCZDKRn59PdHR0L3wM5Xx28gKDdBqmDJNFU0T3qdUqfnbjCN7/2Q1oNSp++e5hdhw3Y7NLd4/oXU5H72i1WlatWsWKFSuw2WwsXbqUuLg4Vq9eTUJCArNnzyY9PZ1HH32UOXPmEBgYyPPPPw/AwYMHefHFF9FoNGg0Gv7whz+g1w+cxUUcDge7Tp7nhhEhMhrDQ1xpOGhPhoJOjNaT+eAsHtl4hI+OlpJ3vpbbpkQzRBZMEb2kW+P0k5OTSU5Obrdt5cqVbd97e3vz4osvdjgvNTWV1NTUayzRfeUZlkMKAAAQFklEQVRdqKWwsoGf3ThC6VJEH7nScNCeDgX189byu7QxBPh4sfXrIl7a+S2LJkYyPmrgNJCE+5Ancq/BR0db73VIf75whYnRen6ZEkeonzd/P2hi06HCPnmQTHgWCf0ecjgcbP26mOuHBhGh91W6HDFABA3WcV/SCG6MD+XQuUpe3PktZ8vqlC5LDCAS+j10orSGb8/XsmBihNKliAFGo1Yxd5yRe2cNR6VS8eruM/xl12kapdUvXEBCv4e2fl2MVq0iLTFc6VLEADU0ZDC/TBnJlGFB/O2AiVvX7JHpmsU1k9DvAbvdwbYjxcyMCyFosIyyEL3HW6th0cRInk0fT1V9M4v+8jkvZn1Li6zOJXpIQr8HDhVUUlTVwK0TpGtH9I3pI4L55P8kMT8xnD9/eoqla/dyyiyLsYurJ6HfA1u+LsJbq2buOKPzg4VwEf0gHS/eMYmX7pjEuYp65q/ezbMfn5C+fnFVJPSvUk1jM1sOF3NzghE/72495iCESy2YEEHWQ8ncOjGCv+zKI/WFbHZ/e0HpskQ/IaF/lTZ+WUhtUwv3zJC584Vygv28+fNtE3l3xVTUKhV3vXaAlX8/jLm6UenShJuT0L8KNruDDXvPcl3sECZEy9OSQnk3jAzhw5WzeHB2HB9+U8pNz33Gmp3fSpeP6JKE/lXYcdyMqaKBe2SFLOFGfLw0PDQnnk8fSmJWXAjPfXKK2f/9L7bnFMt8/aIDCf2r8Pqes0TqfZk71qB0KUJ0EBs8mJfvuo53752Kv4+WX7x7mGXr9nHgbNdLRwrPI6HfTQfzK9h/toL/uGEoWo38ZxPu64YRIWQ+OIs/LU6koKKe217ex/LXD8iDXQLo5iybns5ud5CxLZfwQB9+NC1W6XJEP+XqaZkvsdRbqWlq6bA9KT6E64cNYcvhIt7ad45bXtrDjaNCWTFzGENDBgPg760lUKZx9igS+t2w+XAR3xRZeP72CfjqZN580TO9MS0zQE1TC9mnyrrcnzY+gqDB3uw5Xcae02X86+QFJsUMYfboMBZOipDQ9zAS+k7UW1t45uMTTIgKZOGESKXLEaJHfLw0fH+MgWnDg8k+dYEvzpRzxFTF8dJqHp47imEXW/5i4JPQd+L5T09hrm7iLz+cjFqtcn6CEG7Mz1vL/MRwZowM4V+nzvNJrpl/flPCjaPCuGtaDHEG/3bHS/fPwCOhfwU7cs28svssd06N4bqhQUqXI4TLBPp6ceuESB5MiePl7DN8frqMnSfOExfmx7ThwYwy+qNWqUiKD5HQH2Ak9Ltgqqjn4Y1HGBcRwO9vGat0OUL0iiGDdaSOM5IUF8r+s+V8caacv35xjkBfL6YMHcLYcH+ihgxSukzhQhL6nbA0NPOzd77CbnfwP3dOxsdLbt6Kgc1Xp+HGUWHMigvlRGk1+89WsOP4eXaduMCNo0JJGx/O98caCPDxUrpUcY0k9L+jos7KXa/t55S5hpfv+h6xwXKDSyivt4Z7fpdGrWJcRCDjIgIpr22ixNLArpMXyDpxHi+NiskxQ0iKD2XqsCASIgOv2CDqaigpgFYNLVdYEkDuJfQeCf3LFJTX85M3D1JQUc8rP76OG2XBc+Ememu455UE+3mzeHIk/7UokcOmKj7JLWX3qTKe/fgkAFq1ipFhfsQGDyImaBAxwYOJDRqEIcCHQF8vapua2X+mApWq4wCISTF6DhdUdXltuZfQeyT0aV3k/O39BTz1z+NoVCo23D2FG0aEKF2WEG5BrVbxvdghfC92CL+ZB2W1TRwuqOJrUyUnSmrIu1DHrpMXsHbSdNeoVfh4afD10uDrpcZXp2GQTsuBs+XUNtkYpNMw2Fvb+qdOS4CPVp6F6WXdCv3s7GyefPJJ7HY7y5Yt47777mu332q18thjj3Hs2DH0ej3PP/88UVFRALz88su8//77qNVqfve73zFr1izXf4oestsdfHrczLp/5XG4oIpZcSE8vXQ8EXpfpUsTwm2F+HkzZ6yBOZfNQWW3OzDXNHKuvJ6y2iYsDc0UVNRzvLiGhmYbjc02Gppt1DXZuFDTxMnS1u2d0apVhPp7EzXEF0OAD4YAH4wBPhgCL/4Z4I0hwKfLrqUrdStJt1E3Qt9ms5GRkcGGDRswGAykp6eTkpLCyJEj247ZuHEjAQEBfPrpp2RmZvLcc8/xwgsvcPr0aTIzM8nMzMRsNnP33Xfz8ccfo9Eo95O8xWbnSKGFHcfNfPhNCfnl9UQN8eXppYncdl10p7+KCiGuTK1WER7oS3jgvxtMhZX1XT4pPClGz8GzFdQ326hvslFvbaG2qYWaxhaqG5rx0WmobmjmaFHrv9XG5o6/RegHeWHwv/TDwBtjgA/Bft5YbXbOldXh7aXBR6vB20uNt1aNt1ZDyphQCX1nB+Tk5BAbG0t0dDQAaWlpZGVltQv9nTt38otf/AKA1NRUMjIycDgcZGVlkZaWhk6nIzo6mtjYWHJycpg0aVKvfJgGa2sroqapmZrG1r9AloZmiqsaKKys56S5lhMl1TS12NGqVVw/LIiH5o5ifoJRJlEToo9pNWoCNOpORwQlxYe0DRV1OBxUN7Zgrm6k1NKIubr1q7S6EXN1E+bqRk6UVFNW24TdyUzS/3cb+HipL+ty0uB9WdfTv19f/NJp8NGq8bm479J5PpftaztPq0GjUaFRqdCo239p1SrUqtY/ARwXP1frn1zc5kCnUfd6w9Np6JvNZozGf68FazAYyMnJ6XBMeHh46xtqtfj7+1NZWYnZbGbChAntzjWbza6qvYNZz+yirLap030hft7Ehfnx4+mxTIjWMysulEBfGX4mhLtTqVQE+noR6OtF/HeeGL5ci81OVUMzeedr2f1tGY0tNpqa7TQ222hqsdPUbCNC74tOq6ah2UaD1Xax68ne1v1UVd/cus1qo7HF3nZMX/nx9FgyFib06jWchn5nizB89ydRV8d059zvKioqYsmSJc7K6lTMxa+uNAFfXfx6vUdX6F3v93CfnCvnKnHutVDqus5WEva++KWUo6dgyZs9O7eoqKhbxzkNfaPRSGlpadtrs9lMWFhYh2NKSkowGo20tLRQU1ODXq/v1rnftX///m4VLoQQ4uo57chOTEwkPz8fk8mE1WolMzOTlJSUdsekpKSwefNmAD7++GOmTZuGSqUiJSWFzMxMrFYrJpOJ/Px8xo8f3zufRAghhFNOW/parZZVq1axYsUKbDYbS5cuJS4ujtWrV5OQkMDs2bNJT0/n0UcfZc6cOQQGBvL8888DEBcXx7x585g/fz4ajYZVq1YpOnJHCCE8ncohKycLIYTHkHGKQgjhQST0hRDCg0joXyY7O5vU1FTmzJnD+vXrlS6ng5KSEu666y7mzZtHWloab77Zw7Fdvcxms7Fo0SJ++tOfKl1Kp6qrq3nwwQe5+eabmTdvHocPH1a6pA7eeOMN0tLSuOWWW3jooYdoaur8+ZO+9pvf/Ibp06dzyy23tG2rqqri7rvvZu7cudx9991YLBa3qu/pp5/m5ptvZsGCBTzwwANUV1crVh90XuMlr732GqNGjaKiovPJ9VxBQv+iS9NNvPrqq2RmZrJ9+3ZOnz6tdFntaDQafv3rX/Phhx/yj3/8g3fffdftagR46623GDFihNJldOnJJ59k1qxZfPTRR2zdutXtajWbzbz11lts2rSJ7du3Y7PZyMzMVLosAJYsWcKrr77abtv69euZPn06n3zyCdOnT1e0wdRZfTNmzGD79u1s27aNoUOH8vLLLytUXavOaoTWRt3evXuJiIjo1etL6F90+XQTOp2ubboJdxIWFsa4ceMA8PPzY/jw4b36hHNPlJaW8tlnn5Genq50KZ2qra3l4MGDbfXpdDoCAgIUrqojm81GY2MjLS0tNDY2On2+pa9MmTKFwMDAdtuysrJYtGgRAIsWLWLHjh1KlAZ0Xt/MmTPRalsHKk6cOLHds0NK6KxGgKeeeopHH32016dhkNC/qLPpJtwtUC9XWFjI8ePH201z4Q7+9Kc/8eijj6JWu+dfLZPJRFBQEL/5zW9YtGgRjz/+OPX1nS9OohSDwcA999zDTTfdxMyZM/Hz82PmzJlKl9Wl8vLyth9KYWFhvdo1ca02bdpEUlKS0mV0kJWVRVhYGKNHj+71a7nnv0wF9GTKCKXU1dXx4IMP8tvf/hY/Pz+ly2mza9cugoKCSEjo3blDrkVLSwu5ubnccccdbNmyBV9fX7e7f2OxWMjKyiIrK4vdu3fT0NDA1q1blS6r31u7di0ajYZbb71V6VLaaWhoYN26daxcubJPriehf1FPpoxQQnNzMw8++CALFixg7ty5SpfTzqFDh9i5cycpKSk89NBDfPHFFzzyyCNKl9WO0WjEaDS2/YZ08803k5ubq3BV7e3du5eoqCiCgoLw8vJi7ty5bnmz+ZLg4GDOnz8PwPnz5wkKClK4oo42b97MZ599xnPPPed2jbmCggIKCwtZuHAhKSkplJaWsmTJEi5ccDZTUM9I6F/UnekmlOZwOHj88ccZPnw4d999t9LldPDwww+TnZ3Nzp07+fOf/8y0adN47rnnlC6rndDQUIxGI2fOnAFg3759bncjNyIigiNHjtDQ0IDD4XDLGi+XkpLCli1bANiyZQuzZ89WuKL2srOzeeWVV1i7di2+vu63QNKoUaPYt28fO3fuZOfOnRiNRj744ANCQ0N75XqyXOJFXU034U6++uortm7dSnx8PAsXLgTgoYceIjk5WeHK+pff//73PPLIIzQ3NxMdHc1TTz2ldEntTJgwgdTUVBYvXoxWq2XMmDHcfvvtSpcFtP59O3DgAJWVlSQlJfHLX/6S++67j1/96le8//77hIeHs3r1areqb/369Vit1raG0oQJE8jIyHCrGpctW9Zn15dpGIQQwoNI944QQngQCX0hhPAgEvpCCOFBJPSFEMKDSOgLIYQHkSGbQlxmzJgxxMfH09LSgkajYfHixSxfvhy1Ws3+/ft5/fXXefnllykrK+Pxxx+npKSElpYWIiMjeeWVV5QuXwinJPSFuIyPj0/blAfl5eU8/PDD1NTU8OCDD7Y77sUXX+SGG25g+fLlAJw4caLPaxWiJ6R7R4guBAcH88c//pF33nmnw9xM58+fbzdBX19MlCWEK0joC3EF0dHR2O12ysvL222/8847efzxx7nrrrtYu3atW8/IKsTlJPSFcKKzh9ZnzZrFjh07uO222zhz5gyLFy926ymFhbhEQl+IKzCZTGg0GoKDgzvs0+v1LFiwgGeffZbExEQOHjyoQIVCXB0JfSG6UFFRwRNPPMGdd97ZYTreffv20dDQALSuxlVQUEB4eLgSZQpxVWT0jhCXaWxsZOHChW1DNhcuXNjpNNbHjh3jj3/8IxqNBofDwbJlyxg/frwCFQtxdWSWTSGE8CDSvSOEEB5EQl8IITyIhL4QQngQCX0hhPAgEvpCCOFBJPSFEMKDSOgLIYQHkdAXQggP8v8B3wqOox3/bN8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'bodies': [<matplotlib.collections.PolyCollection at 0x7f61349b6470>],\n",
       " 'cmaxes': <matplotlib.collections.LineCollection at 0x7f6134ad3be0>,\n",
       " 'cmins': <matplotlib.collections.LineCollection at 0x7f6134b013c8>,\n",
       " 'cbars': <matplotlib.collections.LineCollection at 0x7f61349b6b00>}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_values = X.drop(['CHAS','RAD'],axis=1)\n",
    "X_classic = X[['CHAS','RAD']]\n",
    "plt.figure()\n",
    "for cloumn in X_values.columns:\n",
    "    sns.distplot(X[cloumn],bins=30)\n",
    "    plt.show()\n",
    "sns.violinplot(X['CRIM'])\n",
    "plt.show()\n",
    "sns.violinplot(X['B'])\n",
    "plt.show()\n",
    "plt.violinplot(X['ZN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f613199c7f0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(X['NOX'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61318bbac8>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(X['DIS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用离散型直方图查看类别特征的分布情况\n",
    "观察到CHAS两个特征都分布特别不均衡，后续可以作为相关性检验的参考"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in X_classic.columns:\n",
    "    sns.countplot(X_classic[column])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看特征之间两两的相关热图\n",
    "可以看到大部分特征与label的相关性较高。  \n",
    "#### 画出相关系数大于0.75的散点图：\n",
    "-  可以看到，TAX,RAD有很强的相关性\n",
    "-  特征LATAT与MEDV的相关性很高，可以预见，最终的模型该特征的权重会比较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([X,y],axis=1,sort=False)\n",
    "corr = data.corr()\n",
    "corr = abs(corr)\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(corr,mask=corr<0.5,annot=True)\n",
    "plt.show()\n",
    "columns = data.columns\n",
    "for i in range(len(columns)):\n",
    "    for j in range(i+1,len(columns)):\n",
    "        if corr.iloc[i,j] <= 0.75:\n",
    "            continue\n",
    "        plt.scatter(data[columns[i]],data[columns[j]])\n",
    "        plt.xlabel(columns[i])\n",
    "        plt.ylabel(columns[j])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 现在来查看之前观察到的分布较散的几个特征（连续：CRIM，B,ZN；类别：CHAS）\n",
    "首先查看CRIM与label的相关性为0.56，画出他们的散点图,可以看到，CRIM的取值在大于小于30的时候，对label是有一定影响的，所以不作处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f61378f4550>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='CRIM',y='MEDV',data=data,kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看B与label的相关性为0.42，画出他们的散点图。两个的关系没有那么突出，且B值小于150的数并不少，不认为这是噪声，故不作处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 490\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='B',y=\"MEDV\",data=data,kind='scatter')\n",
    "print(len(data['B'][data['B']<150]),len(data['B']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看ZN与label的相关性为0.39，画出箱形图,可以看出ZN值从80-100的取值对label有一定的影响，故不作处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f6134984ac8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='ZN',y='MEDV',data=data,kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61362a3748>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sXr1aaoysrCxkZHC5V6JQdN5mxcK9W2DT7AAAm2bHgr2bMTyhL6JNcqexLSsqRFmTwqQQeb2vlhqz1FaFRYW7UX3pKnSbpmHhnl0YntAL0ab2LRtM5A2qqmD1qlK322dduraovRRVwbf5591uf0NeF4/EDUb1U9eK90vcmNXJlyjOipXWFrDO2gdSMQRILIjCwsIwf/58WCwW1NbWYurUqRg+fDgGDBjQpN3YsWPxX//1X7LSICIiNxQc3w9N6Fic+xDCDUbkLX390v5HBzAhWd4o0XlbJRYWbmtaiBVuw/CEJESbPLtkcOMNT6NNZvxz4m1ut7+c+hObNyvlXcQe6KND56pq0NUc5us0iMiFxsWN0HQohpbvgY1Hf4Jl9E9aQaQoCiyWum8V7XY77HY7FCU4HjRfyMjI4OgNEUkzMqEfMq9IhaVDOADgH+MexIyV72JEfF+pcQuOF0G7tDzW74bnYO7GFdCEQMHxIkxo4yiRs/16WjsNz1l77gfUenvPluO5gn14YUR/9I/p6NXY56vs6GIO3CsEXE1188Soj6em0lHwqdx9ChVbfgQ0gdgHh0BoOs689y0ih1wBy0DvLrojm9R3CE3TMGnSJBw7dgxTp05FWlpaizZr1qzB1q1bceWVV2Lu3Lno0aOHzJSIiMiB+mlxb+1aDwB4IG0U7rxqqPTpciMTkrCocBs+GD8DEcYOAACDomBEQlKb+qu/luelb77yZJpN/O3OuwN+tMabNF3gz9uOAAD+tO0I/pR5DQxeOgHfd9aG5zeewm+H90C/GJNXYnqaqir45JOz0vqfPDnm8o28pP71e2x/y5XrPMV8+Dq+ft1k6tsNFV//C4DA6b9uqTtoVGHq282neckgtSAyGAzIz8/HxYsX8dBDD+HgwYPo06dPw+9HjRqFcePGISwsDB999BGeeuopLFiwQGZKRETkQP30sAfSRjUcy+s9qFXTxtoi2hSB6SnpMBs7QFEULB4/A1/8eMDj0+XIdz4vKkGZrRYAUGarxcqiEozrLX8lQU0XeHPbGQDAm9+fwasZ8V4rxIiCgSEiDJE39qoriuw6FKMKy429YIho29RXf74e3itjyJ06dcINN9yATZs2NSmIoqN/Guq99dZb8corr3gjHSIiasagqvjNliUtjj8/ZKL02LnJKVAvTak2G40Yl5TS5r7qr+X5a6X7F5u3JQa5p9RWg48KT6Baq9sQp1rT8WHhCQxL6IooUwepsVcVXcSF6rpFM8psGlYXXcTY3twfx5/Vv35/OHFaYgz/2m9MaAJxs1Ol9q8Y2v5FQETaz1C1+xS0CzaoljBEpP3Mg9n5D2kF0fnz52E0GtGpUyfYbDZs2bIF9913X5M2p0+fRmxs3RNz3bp1SEpq2xQJIiJ/Vz8VpGKXvP0mDqcebtXJevPRn8sVP7JGiwyKird31U3HuD9tCMAv8YPGV8fPQ790jdjzw/vhNxv3QxcCXx0/J3WUqMxmx+K9pajW6mJXawL/2FuKIQmRiDIFzgbW9e8bu3dflBYjLS2dRb4PuSpWzrx3wO1+ut3r+HrP9hRDQN2iCZ1+3huln/yAThm927WIgj9fDy+tIDp9+jSefvppaJoGIQSys7MxatQovP7660hNTcXNN9+MhQsXYt26dTAYDOjcuTN+97vfyUqHiIiaMagq7l3zvtvt38u8W2I2gc+u61g2Rd6IWiAu5nBTQld8VHgCC8YPhKWDEQvGD8J9K3ZiWEJXqXE3H7dCFwIfTLiiYZqctUZDwbEKjE3mKBH5P6ELp0WOs/ayVnwL69kZMXdfB0NkuJT+/YG0gqhfv35YunRpi+OPPPJIw8+PP/44Hn/8cVkpEBH5jfqpIH+ukXexfyB+y1s/6nR/2hCHxwOJq2LlF6vWut3PO9mOv0ENtGIIAKJMHTAvdxDCLi3dazYaMDUlXvp0uaykTshpNj3OEmZAZmInqXEbK6/S0NHcvtGo+veNqip5iyokJ/vPogr0k6bLXzue9tb4uOzlr4O5GAK8dA0RERGRIwZVxW82r2px/Pmh2T7IRg5N150WOc7aB1ox6Iymi4ZiCABURcG43nHQdOHxBQ4a92l00nfj4zJyqHfibA0WbzqP227qgp4x3HuJ2qdy5zmUf1WC2Pv7Qb005VO3aajaWwrLIBa0nhD0BVHjzaM6jh7i8DgREZEsjYubaruGcGPLUYPGx4OlGAIAg6rghc0tr4P49VDP729lUBU8vMr9a/T+nJ3g8RyAun19Vn9ft2z0qu0XMPPnMQG5z4+r/YluyOsitX9qytQvChVbSnD6r/sajikdFMTcLXefuFAS9AWRoqooy285VSEqzz8v6iIKBBWVZxEZwW+lAoVd12BUHU/d8cR1Qa76p6Yq7bWYtmwFbJrWcMxkMOCdMZkOCyVqya4LpyNAnihyXPXvjh1FlbBW162qZ7Xp2HmkEoOS5e7nJYOqKtiw6Iy0/kdOC769bGQxWIyIHNIdFZtLIOwCilFB5NDuMFiC/jTea/hIEvk5XdegNjrZzPj5C62+jSedPP0D/vnFHEz5+R/QM1beUqHkOUbVgJxPX5XW/4pJs6X1HWyiTSZMS+mPhYX7UK1pCDcYMC31KkSbAnPTUEeaT0W73GhQa6euGVUF//np0Tbndzn/N+nKNt/WatOwZV8F7JdWt7NrApv3VqBvvAmWAFrdjvxPxMCuqNx5HtqFGqiWDogYIHdhklDDgojIz6mqAZ98OrNVt5k86e9SctF1DWu+qdsvbO03r+DOcX+TVngRBatxyUlYXnQUxVYrok0mjEtK9HVKHmVQFTy0epfb7f+SlSYxG+/af8IGXQg8NC4W4R1UVNfqeOvz0zhwwhaQo0TkPxRVQeesnji/+Cg6Z/eUvohCqGFB5AVaRTkMkR19nQZRu+06mI/KqjIAgLWqFLsOfoaB/eRv3EnBpcX+R5dZQCGYFhkA6q4RejR9EJ4u2IRHrxsUVPct1A1IjMC1jQqf8A4qHsmrW0SCqL3C4i3odl9fGDrKXaUxFLEgkqzm1AmU5S9GVN5tCOvR09fpELWZteo8tuyeD7tmAwDYNRu27J6HPr1GwmKO9nF2gcGua/h8wotS+w+Ea3kMqor7Vv/D7fbvZt0mMRvfSOkWg3k52ehqNvs6FfIgg6pgyZbSFscnDuF7JHkGiyE5WBBJJHQdF9etBgBcXL8KXf9zJle2o4B14F8bIHQNs25fCQB446MxELqOg/9aj4H9Jvk4u8DgTrFSaivHPWtfgU2rbThmMoThbxmPI9rkeqQ5EIohb7DrOpZPuUNq/57YE4jFUHBovlra5Yofrq5G5H9YEElUuWcH9CorAECvtKJqz05EXDPIx1kFDl23Y9S9K6T2r6p8Cbirb69R+Hr3/CbHFFVFn16jfJRRcIo2dcT0/hlYuG8tbFotwg0dcGf/jMsWQ/QTV8XKfas+c7ufd7PHt7p/Cj2qquD9Ne6vxnZ3JldXc0XoAlf/IlZq/7z+hprj2aAkWqUV1u+2AHZ73QG7HRXfbYYpuS/UCF5Y6Q5Xxco3i+91u5/Bt77X6v6pJYs5GkOumQFFqTsZNBpMGHLNXZwuJ8H4xBux/Mg3OFV5Hl3COyI3cbCvUyJqwdXy1J5YKKG9y19TYHJVrOz/8Kzb/fSb6nhrCBZD5AjPCCWpPrQfEHX7EESNvwVln30MCB22wwc4SkQBK61PXsPP9078EB2MwbNUsD8xqAY8NmgynvzqXcy+dgoMnApHfsioKpj8yXfS+v9k8vXS+iYiaowFkSSm3v1QsXULAA0desQjZuZDOLvwHZiSuaswBS5VNWDDtr8CAEamP+jjbIJbasyVWJD1FGLMndvVz+HDh3Hw4EGU7z7socwcxLjmMJKTk6X1T1Sv/vlcuvu0vBjXaA6fz672S/LENLjW7sdERJ7DgkgSNcICy/VDYP1uMwBACQtD5PVDOV0uCFVZz8JscTw0H0zqN3ttXgjJ3AS2uQtVZ9HZHPyPdb32FkNE5DkGVcGrS4ql9T97Ypy0vonINRZEEkWkDkTVnp1QlLpvfMypA3ycEXna2eI92PT5E7hp7O8RE5fq63SkUlUD8tf/usXxvFEveCX+j2f24N0Nc3DfqFdwRUxwP9aelJycjOTkZPzFtl9qDAoc56ps6GoOzOmu9c/nBbajEmNcKa1vCh5auQZDR05nDhYsiCRSVBVdb7vL12mQJLqu4ftNfwAAbN/0Kn4++d12jZS4WvVu8qS/t7lfd2P4M03X8M+trwAAPvnuD3g0+z1eV0PUBoVnSvFswTa8ODIdV8VwQRQKHEIXThdKcNZe1gIKNcdrcO6jc+g6tSvC4sOkxCDvCrwzowCjGAwo/2o9AKDjMC5PHEyK9n6G6qoyAICtqhRH9i1DcsqENvenqkbMm5/pqfQcumvGGqn9y/LN4c9QYat7rCtspfjm8GcY2meij7OiQKPputOltJ21NwTREtuaruONbYUAgDe2FuIvWUOC6v5RcGte3BxZ1nID3MTcn4p8WcWQ0AXKPq/7PCr7vAzd7u3GleuCQFAWRELXm2yAGpWX0ar2RJdjqzyPfdsXQLPbAACa3Ya9389HfOIImLgMtUeV285j7Z75qNXqHusazYa1e+Yj7T9GItLEx9qfudrA9N2s26T270jzk//fXPqyqrHnG31xFWzFworDx1FmqwYAlNpq8HnRceT27uXjrIgCi3W7FXpl3SrCulWHdbsVkemRPs6K2isoCyJFVXFu0RK323edxm+aqXVOHCmAEBpy7vgYYeF1b4Q11RU4dngtklOC5/nUfMGEy10vJGOBhd3HNuCpcYtgDqt7nKtqKvC/y6Zi17ENHCXyc0ZVRc4n70rrf8Xk+6T1HWxKbdX4oLAI1VrdiVy1pmHRniLclBCHKFO4j7Pzf/Wr2x3eVSYvxtXX83o8NwldNBkNanxc5miNVqGhYlMFRK2oi1crULGpAub+ZhgsnMYdyIKyIPIHjUedGk+V42hUcEi8KhfJqU1PxsPCI5HYP7dN/dV/2O7da/VEei7jOPrAdXZtUWuLG2ft23Pt0uDk8TA0uq05LBLPT/4Mmm5vU39EoSjCaMQ/Jo5ucbzarvkgG6L2UVQFZ3db0TU1oqEA0u0CqlHu1DU1XEXcYy1XA6wvkChwsSCSRFFVlK34tMXxqJxJPsim7dauXYvVq1dLjZGVlYWMDNfTGv1B49EPZyf3jY97cznq9lJVI17/MEta/49Mbd1zSNO1hkUTDE4e68bHG7cnojqNr4EKNzp+fTQ+HmzXTHlS/ep2+6vlLbudnOw/y27Xf0lXWHhRWoz4w+ltHhGrrdRQvM2K6L5mGMIVCCGwd8EZ9P3PGHSIaNtzWGgCisF1QaV0cPx7Z8db2z/5DgsiIjepqgGrF89wu33WrfPdblv/YXv6zB/bklqr4gQCg2rAqyvvdrv97DHvS8wm8Nl1DSsmzZbavzHAClJN15tcL9T4eKAVBc6upWrt/XDW3lH/9SfMF2Ru+JvWJWDes2TSdYHJk+Xtv6brAmqALQpw4XA1IIC9888i9d5u0GsFIIALRTbEXB3R6v4aNvz9rOVCDZ5yw5wb+Hz2YyyIyKWMjIyAGL3xJFfTu1pT5LSl/1Cj6XanI0CeKHJc9R9KXBUr965x/3F+L9NxkRpoxRDw08n/2zu3AQDuH5De5HggMaoqJvxzrbT+l04Jrc8Af+OqWFm9yv0T+Kxsx4vQOOq//ku6iPNn3O6/tZKTu7X5tp2TTSjeVgEA2PNeXY6KEeicFJj7a5Hv8UyBqBlVNWLJ+2Ok9T/x7pXS+g40BtWIZz/Oltb//9yyyuFxu26HUWKhJLt/8gxN6A2FkC4EhBABVxDVf7N9cfdueTEGXNnim+36E+a/VX0nLa6/fZuu6QKzJ8qb1qbpAoYAG6nxlQ4RKuLSLSjeZoWw1xVDcddFtnm6XHJyMpKuTIIyVt7jLzReZ+TP+IlNAUfXNQy+9b1WtQ+Ua3l8of6E6tAeeQs6OFvMwVeMqhHZn42V1v+q8Z9L65s8Z9mhAxjfux9URUGVvRZf/HgEeb37+Tot8lOuipX317g/knJ3puORERZDrdM1NQLnCqtQU66jQ4SKrinmdvXn7PrUlSLNAAAgAElEQVSe1q5c56w9rx/ybwFdELlasc0TS2lzRTj/1Ly42bXqNy3apGU/77Q9+Yf6Quz4DxXyYgxsWYjVx63YKTHuVf5VADqj6brTaXDO2gfaCIozpbYqLCrcjYwrkxBuMOK2/I9hMhgxPKEXok3tO7HypvqRmncqO0iN4YhdF/hk8vXS4tp1AWMAFAm6LpwWOc7aB9o1O/5IURXEj+yEI8vKED+yk7Tlthv3W7G1AuUbyxH3WNxPq9vZdFTtqYIl3dKiPQWOgC6IFFXFmTcXSeu/2y+ntap9iw1hL7OiHAsu8gf1J1SHyl6XGoP8T4uNSre03L/t+SE/fbkULMUQABQc+xGa0HFb/scNxzShY+PxfwXcKJFd16Ve5+Ns0QZXxcpDq3e53f9fstIcHg+EYghoeg2Os2lvjY+zGPIcS48w9LujKzp4aQ8g81VmVHxVgeJXflptUOmgoNsv2n49FPmHgC6I/I2iqjj3ofsXKHed6v43s0TBpr4Q22x9Q2oMZ3HfsP/Zq3H9nabrTYqfxseDqRCqN+I/rsCiwt2ohd5wzKCoGJ7Qy4dZtY2jYgVo/d/OWXtn/TvvRzgtcpy1b810Mbsu8H+TrmxVTq3RnpGpnUcq8VVhOX6V272h8LHV6tj7ryoMSrZ4Mk26xFvFEAAYLAZE3hTZsDmr0kFB5E2R3JQ1CLAgImqkfjrVrn3yplNd7WfX0xABwIWaKoQbjLB0CAcAVNTYMGPlu3gv625Em4LvRC7aZMa0lGuwsHAXqjUN4QYDpqemBdR0uctpXNx8dvBfuPmKn8ESVje1TtMF/nPpOky/Ohnje/dq0b59cX8qJpyNLjUuOlp77YyrYuXhVcfd7ufP2Qmt7v9y+sWbsGVvBf6YX9JwrINBwd2Z8pbNJu+yDLKgcnsltDINqkWFZVDwvT+GIhZEbSB0DYqT61I8Merjqn8iaj+7bpe68EEgrjJXcHw/5hd+hU/zfgUAuG35X9FBNaDgxAFMSB7k4+zkyE3ui+VFB1FsrUC0yYxxSX18nZI0w/8jDnev2IRqTWs4Fm4wYHiC3M1AVxadxuheMbCE1b0eNF3gjvxtmJaagHG9/WcjUk+xmAwY0j8Sm/dVwK4JGA0Khl4VCYuJn+nBQlEVRI2JwrmPziFqbBSvGQoSgfWJ7ScU1YDTb70qrf/YB+RtoEiu1U+nqi7+k9QYVEfT7U6XxvZU/472IXJVrNz95T1u9//+zX9zeDzQiiEAGJnQD4v2bkHukp82BzYoKkbE9/VhVnIZVBWPpt+IpwvW4rHrbgzKqYH1okzhuCMlCR8UHka1piPcYMC01CREmcKlxr0poSvu+3wnqrWfpiaGG1QMS+gqNa4vDUyKwM6jlbhg1WAxqRiQ2PqNQsm/hSWEIfaXsTB0ZKEbLALvU5soSOm6HXfNWCM9hj9tCutq09RXV7o/2upsE1duyuq+aJMF068agoV7N8Om2RFuMOLOq4YG5XS5xlK7xWJezkTEmIP/pDUnOQGfFx1HsbUK0aYwjE1yPGXMk6JMHXB7Sjw+KjxxqRBTMTUlHlEmeavi+ZqqKsga1BmLN51H9qDOXEQhSLEYCi48W6CAJnStyRLbjY8H2rRDV4XKJ5/ObFVfkyf9vdUx/Imma06LHGftDR74e2u65nTUR2Zcf5GbNBDLjuxEsfUCupgsGJc0wNcpeUUoFENA3YjYr9JT8GzBNsy6LsVrI2Jjk7pjZVEJSqzViDJ1wJik7l6J60vxMWG4L7sbOprlvT/oukBWdnSr2rM4I3IsMM6OiJyosV3AN4vvw5Db56NDeCQAwF5tha7VICzC/Q8K8i/Ni4z5m37dos2Mm15w2t5TcZ/75r9atPnvwf/P43H9hUFV8di1WXhq42I8dm12UE8hC1Up3aLxt5yb0NVs8lpMg6rg4fREPFewD79KTwyZDUhlFkNAy+W7N2++2KLN0KGdnLYnop+wIKKAdrqoAEJo+GrhbRh5zzIAwOYP70DidXchIXWCj7MjT9B0e5Pip/FxmVPiNF1rUvw0Ph5shVBjqTHxmD/mPsSYO/o6FZLEm8VQvatiOuKdsQPQ1Rzm9djUNrouMHKavP11OGJF/oQFEQW07kkjceT7RRCoxYa/5QIAVKMJ3RNH+Dgz8pRvDn+GNT/8Hb+dlA8A+PU/x8KohiHz6pkY2qflvjmeUl/0vPnDWwCAX179AJYULcXEpOAvtFkMkQyyiyFNF06X0nbWPlRGq9rCVbHybf55t/u5Ia9Lq/sn8jYWRBTQwiKiceW103H0+4XQ7TaoxnAkpt/Zrulyum7HxLtXejDLlv3727U8um7HI1NXS+2/rfc57T9GYe2eBfj1P8c2HFMUFWn/MdJD2Tl23nYeYYZw/PLqBwAAFbUVmL9/AUb2HIFoE6djEvmb5sXN8xtPtWjzm+E9nLYnotDlX2dlAaB+484LBw5Li3EdN+5slfiUXJzcuwy28mKEmbug51Xj2tWfsxN3XdegtmKqlLP2/lYMAa5zmveZ+ws63DXe84s5RJqikZE6A2v3zEetZkMHgwkZqTMQKbko2XCyAH/fNw/5OUsAAFNW3oowtQM2nCwIiVEiIgocui6aXC/U+DhHYoguT9qZWXV1Ne644w7U1NRA0zRkZWVh1qxZTdrU1NTgySefRGFhIaKiovDaa68hPj5eVkoUpFTVgP4jHsOO5U+h/4jHWlW0tDZOPWcjHo2Py8ojFA1OHo9vDn+G89ZT6GiKxuDk8dJjjuo5Egv2L8TYZT8V2KpiwMienI5JRP6lcdGzc6cVADBggEVKMSR04XQanLP23LyU/J20gigsLAzz58+HxWJBbW0tpk6diuHDh2PAgJ+Wcf3444/RqVMnrF27FitWrMArr7yCP/7xjy56/Un9SM3FA4Wy7gLSHYzU1G/cefrYfmlxYzk61GpRcakYcvt8hFtivBLvyN5lKPz+79C1WkyYuQIA8NmCCUhJn4nkFHnXtYQqg2rA5Osex7sb5mDy9Y97ZVGDaFM0ZvSbjvn7F8Km2WAyhGNGvzs5Xc5N9e/R5bvlvVceTuNoOjmm6aLJ9LjGx4N1qpy3RoOaFzc/rL/Qos3Vozo7bU/kj1yuqfrDDz+0uWNFUWCx1G3oZ7fbYbfboShNXxTr1q3DxIl1J49ZWVn4+uuvIYRoc0wKbd4qhgAgPmkkFKXupHzp33Ow9O85UBQD4hNHei2HUHNFt1Q8OW4RrohJ9VrM8VeOR3R4XQEUHd4F46/M9VpsImq7xkXPX7edwV+3nWlxPNgUFVVBCIEBAywYMMACTeP5FJG7XI4QPffcc6isrEROTg5ycnJa/U2cpmmYNGkSjh07hqlTpyItLa3J70tKStCjR903OEajER07dkRpaSm6dLn8UGz9SM2Zf7m/0klrdeM3j+SEyRyN/oPuxL7tC6DZbTAYTbjq2hkwmTl6IFNns/eKXqBuZOrxgY9hzuYn8fjA2UG93Lan1b9H/6XqpNQYRM4E82hQczabjsLCSvTqZUJYmIKaGh2ff34e2dldYDLJ209M6KLJaFDj4xwZokDi8lWydOlSvPXWWzAYDJg1axby8vLwzjvv4ORJ9z7gDAYD8vPzUVBQgN27d+PgwYNNfu9oNKj5KBKRv0q6anxDAWQyRyOxP0cPglFq11QsyliA1K4pvk6FiFphVdFF6ELgwfRueDC9G2o03dcpSXP8eDWEAJYtOw+rVcOyZechRN1xmRoXPYe3VuDw1ooWx4kCwWWvIUpMTMTDDz+Mhx9+GPv378eKFSswY8YMxMTE4P/+7//cCtKpUyfccMMN2LRpE/r06dNwPC4uDqdOnUJcXBzsdjvKy8sRFRXV9ntD5EWqasCgm2Zj0+dPYNBNs6UtoqDrGiZPcrx6m6vbeCIfXdecrhwnM66/ifHyyBQRtU+ZzY7Fe0sxslckLGEGWGs0PLjyOF7PSkCUKfjeoxISwlFYWLeYwqpVpQAARak7LpuuCygKkHxdJACODlFgcntRBV3Xce7cOZw9exZVVVWXndZ2/vx5GI1GdOrUCTabDVu2bMF9993XpM3o0aOxZMkSDBw4EKtXr8bgwYM5QkQBJSYuFdm3LYRZ4vVLzQuMtV/82mG7jJ+/4PQ2noqdv75l7LxRno9LRNQem49boQuBe5Yfw3/dFIf/t6kYHVRgy4kKjE1uOcUr0JlMKlJSIlBYWAlNAwwGICUlQup0uXonD1ShR5IJxjAFQgicOFCFhP4R0uMSedJlC6Jt27Zh+fLl+OKLL9CnTx+MHTsWzzzzDDp2dL2T+enTp/H0009D0zQIIZCdnY1Ro0bh9ddfR2pqKm6++WZMmTIFTzzxBDIyMtC5c2e89tprHrtjRN4isxgiCmR2XceKyfddvmE7+jeq8k/4KPAMTYjE4r2lAAT+36ZiAICqKBgSH+nbxCRKSjKjqMgGq1WHyaQiKcksPWZ1lY6ju6wo+t6K4VNjoNsFju6yovsVJoSZ+dqkwOGyIBoxYgR69OiBnJwcPPzww4iJcf/Er1+/fli6dGmL44888kjDz+Hh4XjjjTdakS5RaNN1rclIUPPfcYSG/ImrYuW+1f9wu593s25rdf8U2qJMBtx6VTQW7y1FtSYQblBw21XRQTldrp6qKkhP74iCggtIT+/olSW4T/9og7h0adbGD88CABQDUPKjjaNEFFBcFkQffvghevbs6a1ciOgyVNWAyqrzMBrCEBZW901ndU0FPv54KiZPmg8zV7kjIgIAZCd1wpojF1FitSPKZEBWUidfpyRdTEwHjB0bDbPZO4Vf9ytMOLrL2lAUAXXXLnW/wuSV+ESe4rIg6tmzJ5YsWYIFCxbg6NGjAOoWWbjzzjsxYcIEryToj4SuIfaB2VL7V/hNPzlx9Mh6bN/+PqZNWwYA+OijSTAYwnDk6HqkXDXJx9kRXZ6m605HfZy1N3A0iFrJoCp44NpueH7jKfzy2m4hswS3t4ohAAgzq7gyzYKjO63QNUA1AlemWThdjgKOy4Jo6dKlmD9/Pp5++mmkpKRACIHCwkL8/ve/B4CQLYpcFSvnPnzf7X66Tr271f0TJSaOxo6d8zF/QXbDMUVRkXjlKKlxdV1rsoBC4+Ocqket0by4+c3mVS3aPD/0p+c3iyFqq/4xJvx1TAK6mN1eQ4paqWdfM04eqIKtQkeYSUXPvvKvXSLytMtOmfvzn/+M+Pj4hmM33ngj3njjDcyePTtkCyIiXzKbozFwwF3YsXMe7HYbjEYTBg68S/p0ufqiZ8O2vwIARqY/2OQ4UVvYdb1J8dP4OK8RIk9gMSSXqiroN6Qjdq65gH5DOnnl2iUiT3P5aVNRUdGkGKoXHx+PiooKaUkRkWv9++fBbKorgMymaPTvl+fjjIjaZllRISYseR/6pYsQrLXVmLDkfSwvKvRxZkTkrqjYMNw4qQuiYjv4OhWiNnH5tYnJ5PyiOFe/IyK5VNWAYcPmYOWqxzFs2BMcpaGANTIhCYsKtyH30781HDMZjBiRkOTDrIiotcIj+DlEgctlQVRUVITc3FyHvzt+/LiUhAKZ0HWn1wU5a69wSgi1UffuV+OWKR/C4sV9kHShN0yV07RaGAz8NpDaJ9oUgekp6VhYuA02zY5wgxF3pqQj2sQle4mIyDtcFkSff/65t/IICs2Lm7IVn7ZoE5Xz0ypgLIaovbxZDAEAhAAuTQ9fuOI+3DnubxydonbLTUrBsqJCFFvL0cVkxrikFF+nREREIcTlGbnNZkPPnj3Rs2dPdOvWreHnnj174syZM97KkYj8xK6D+RBChxA6rFWl2HXwM1+nREHAoKp4LH0EAOCx9JFcVY6IiLzK5afOnDlzGn6+7bame0Y8//zzcjIiIr9krTqPLbvnN/zfrtmwZfc8WKtKfZgVBYvUmB6YP3YqUmLifJ0KERGFGJdT5oQQDn929H8iCm4H/rUBQtfwxkdjGo4JXcfBf63HwH7cEJbaL8Zs8XUKREQUglwWRIqiOPzZ0f+pKaHrTa4Xanyc1w5RIOrbaxS+3j0f0Gsbjimqij695G4IS0RERCSTy4KouLgYL7zwAoQQDT8DdaNDJSUlXkkwUCmq2lD8lH+1HgDQcdgoFkMUsCzmaAy5Zga27JoPu2aD0WDCkGvugkXyhrBEREREMrksiJ588smGn1NTU5v8rvn/qaXKPTtg7puKjsPqvkEXdjsUI3fMpsCV1icPuw5+hgsVp2AxRyOtz3hfp0RERETULi7PzidOnOitPIKOVmmF9bstsG4pQLf7HwMAnJn3JmKm3g01gvPkKTCpqgEZgx/HP7+Yg4zBc7jkNhEREQU8lwXRAw884PLGb731lkeTCSbVh/YDQq/7j9Ahau2A0GE7fAAR1wzybXJE7dAz9mrcM+EDREZ4eQ8kIiIiIglcFkQ7d+5Ejx49kJOTg7S0NL9bWU7oOrr9cprU/tt6zY+pdz9UbN0CQMOZd16vO2jsAFNyX88lSOQjLIaIiIgoWLgsiDZv3ozNmzdjxYoVWL58OUaMGIFx48ahd+/e3srPJVfFyrlFS9zup+s0x1MD27MAghphgeX6IbB+txmw2wGjEZHXD+V0OSIiIiIiP+LyjN9gMGD48OF46aWXsHjxYvTq1QvTp0/HwoULvZVfQItIHdhQAKkRFphTB/g4IyIiIiIiauyyS57V1NRgw4YNWL58OU6ePInp06cjMzPTG7kFPEVV0WlUFsryF6PTqGwuuU1ERERE5GdcFkRPPfUUDh06hJtuugkPP/ww+vTp4628gkZYj3h0nXYfDJEdfZ0KERERERE147Igys/Ph9lsxtGjR5tMkxNCQFEUbN++XXqCwYDFEBERERGRf3JZEO3fv99beRAREREREXkdL2ohIiIiIqKQxYKIiIiIiIhCFgsiIiIiIiIKWSyIiIiIiIgoZLEgIiIiIiKikMWCiIiIiIiIQhYLIiIiIiIiClksiIiIiIiIKGSxICIiIiIiopBl9HUCMghdR9dpE1vVXlFZGxIRERERhZqgLIiaFzdl+WtbtInKy3DanoiIiIiIQgMrASIiIiIiClksiIiIiIiIKGRJmzJ36tQpPPnkkzh79ixUVcWtt96KGTNmNGnz7bff4sEHH0R8fDwAICMjAw8//LCslIiIiIiIiJqQVhAZDAY8/fTTSElJQUVFBSZPnoyhQ4ciOTm5Sbv09HS8/fbbstIgIiIiIiJyStqUudjYWKSkpAAAIiMjkZiYiJKSElnhiIiIiIiIWs0r1xCdOHEC+/btQ1paWovf7dy5E+PHj8e9996LQ4cOeSMdIiIiIiIiAF5YdttqtWLWrFl45plnEBkZ2eR3KSkpWLduHSwWCwoKCvDQQw9hzZo1slMiIiIiIiICIHmEqLa2FrNmzUJubi4yMzNb/D4yMhIWiwUAMGLECNjtdpw/f15mSkRERERERA2kFURCCDz77LNITEzEzJkzHbY5c+YMhBAAgN27d0PXdURHR8tKiYiIiIiIqAlpU+a+//575Ofno0+fPsjLywMAzJ49G//+978BALfffjtWr16Njz76CAaDASaTCa+++ioURZGVEhERERERURPSCqL09HQcOHDAZZtp06Zh2rRpslIgIiIiIiJyySurzBEREREREfkjFkRERERERBSyWBAREREREVHIYkFEREREREQhiwURERERERGFLBZEREREREQUslgQERERERFRyJK2D5G/ELqOqLwMh8cVlfUgEREREVEoC/qKoHHRU75uC8rXbWlxnIiIiIiIQhOrAiIiIiIiClksiIiIiIiIKGSxICIiIiIiopDFgoiIiIiIiEIWCyIiIiIiIgpZLIiIiIiIiChksSAiIiIiIqKQxYKIiIiIiIhCFgsiIiIiIiIKWSyIiIiIiIgoZLEgIiIiIiKikMWCiIiIiIiIQpbR1wl4i9B1dBw9pOFnRWUtSEREREQU6kKmKlBUFdatuxt+JiIiIiIiYmVAREREREQhiwURERERERGFLBZEREREREQUslgQERERERFRyGJBREREREREIYsFERERERERhSwWREREREREFLJYEBERERERUchiQURERERERCErZAoiIQQs110DIYSvUyEiIiIiIj8RMgWRoihN/iUiIiIiIgqZgoiIiIiIiKg5FkRERERERBSyWBAREREREVHIklYQnTp1CtOnT8eYMWOQk5OD+fPnt2gjhMALL7yAjIwM5ObmorCwUFY6RERERERELRhldWwwGPD0008jJSUFFRUVmDx5MoYOHYrk5OSGNhs3bsSPP/6INWvWYNeuXfjtb3+Ljz/+WFZKRERERERETUgbIYqNjUVKSgoAIDIyEomJiSgpKWnS5ssvv8SECROgKAoGDBiAixcv4vTp07JSIiIiIiIiasIr1xCdOHEC+/btQ1paWpPjJSUliIuLa/h/XFxci6KJiIiIiIhIFukFkdVqxaxZs/DMM88gMjKyye8cbZLKfYKIiIiIiMhbpBZEtbW1mDVrFnJzc5GZmdni93FxcSguLm74f3FxMWJjY2WmRERERERE1EBaQSSEwLPPPovExETMnDnTYZvRo0dj6dKlEEJg586d6NixIwsiIiIiIiLyGmmrzH3//ffIz89Hnz59kJeXBwCYPXs2/v3vfwMAbr/9dowYMQIFBQXIyMiA2WzGiy++KCsdAD9N0eO0PCIiIiIiAiQWROnp6Thw4IDLNoqi4De/+Y2sFBzGIyIiIiIiqueVVeaIiIiIiIj8EQsiIiIiIiIKWSyIiIiIiIgoZLEgIiIiIiKikMWCiIiIiIiIQhYLIiIiIiIiClksiIiIiIiIKGSxICIiIiIiopDFgoiIiIiIiEIWCyIiIiIiIgpZLIiIiIiIiChksSAiIiIiIqKQxYKIiIiIiIhCFgsiIiIiIiIKWSyIiIiIiIgoZLEgIiIiIiKikMWCiIiIiIiIQhYLIiIiIiIiClksiIiIiIiIKGSxICIiIiIiopDFgoiIiIiIiEIWCyIiIiIiIgpZLIiIiIiIiChksSAiIiIiIqKQZfR1Aq118uRJTJo0yddpEBERERGRnzp58qTbbRUhhJCYCxERERERkd/ilDkiIiIiIgpZLIiIiIiIiChksSAiIiIiIqKQxYKIiIiIiIhCFgsiIiIiIiIKWUFZEG3cuBFZWVnIyMjAO++80+L3NTU1ePTRR5GRkYFbbrkFJ06caFOcuXPn4sYbb8S4ceMajr300kvIzs5Gbm4uHnroIVy8eNHhbUePHo3c3Fzk5eW1ehlxR3EfffRR5OXlIS8vD6NHj0ZeXp5X4u7btw+33nprQ3+7d+92eNslS5YgMzMTmZmZWLJkSavinjp1CtOnT8eYMWOQk5OD+fPnAwDKysowc+ZMZGZmYubMmbhw4YJHYzuLu3LlSuTk5KBfv3744YcfnN7+cs/D1sZ197nl6bh/+tOfcNNNNzU8vwoKCrwSV/Zzq7q6GlOmTMH48eORk5ODN954AwBw/Phx3HLLLcjMzMSjjz6Kmpoah7d/++23kZGRgaysLGzatKndcZ955hmMHz8eubm5mDVrFqxWq1fi1vvv//5vDBw40OntPR336aefbnivysvLw759+xzevj3vHQCgaRomTJiA+++/H4D8v6+zuN66v47e473xueTotrLfOwDg4sWLmDVrFrKzszFmzBjs2LFD+mfDkSNHGu5TXl4eBg0ahHnz5vksrjce53nz5iEnJwfjxo3D7NmzUV1d7ZXXkqO4jz/+OLKysjBu3DjMnTsXtbW1Dm/bv3//hsfkgQceaHdcb7yG58+fj3HjxiEnJwfz5s0DIP9cx1lcGc8rR+eRzu6fEAIvvPACMjIykJubi8LCQod97tmzB7m5ucjIyMALL7yANi+eLYKM3W4XN998szh27Jiorq4Wubm54tChQ03aLFq0SDz33HNCCCGWL18uHnnkkTbF+u6778SePXtETk5Ow7FNmzaJ2tpaIYQQL7/8snj55Zcd3nbUqFHi3LlzHovb2O9+9zvxpz/9yStxZ86cKTZs2CCEEGLDhg1i2rRpLW5XWloqRo8eLUpLS0VZWZkYPXq0KCsrcztuSUmJ2LNnjxBCiPLycpGZmSkOHTokXnrpJfH2228LIYR4++23HT7W7YntLO7hw4dFUVGRmDZtmti9e7fD27rzPGxtXHeeWzLivvHGG+K9995zeVsZcWU/t3RdFxUVFUIIIWpqasSUKVPEjh07xKxZs8Ty5cuFEEI899xz4oMPPmhx20OHDonc3FxRXV0tjh07Jm6++WZht9vbFbe8vLyhzYsvvtjw3JYdVwghdu/eLebMmSMGDBjg8LYy4j711FNi5cqVLm/b3vcOIYR4//33xezZs8UvfvELIYSQ/vd1Ftdb99fRe7w3Ppcc3Vb2e4cQQjz55JNi8eLFQgghqqurxYULF6R/NjTPf8iQIeLEiRM+iyv7cS4uLhajRo0SVVVVQoi619Ann3wi/bXkLO6GDRuErutC13Xx2GOPOYwrhHD6ftbWuLJfwwcOHBA5OTmisrJS1NbWihkzZoijR49Kf145iyvjeeXoPNLZ/duwYYO45557hK7rYseOHWLKlCkO+5w8ebLYvn270HVd3HPPPQ3nDa0VdCNEu3fvRq9evZCQkICwsDDk5OTgyy+/bNJm3bp1mDhxIgAgKysLX3/9dZsqyuuuuw6dO3ducmzYsGEwGuv2ux0wYACKi4vbeE9aF7eeEAIrV65sUn3LjKsoSsM32eXl5YiNjW1xu6+++gpDhw5FVFQUOnfujKFDh7bqW6LY2FikpKQAACIjI5GYmIiSkhJ8+eWXmDBhAgBgwoQJ+OKLLzwa21ncpKQkJCYmurytO8/D1sZ157klI647ZMSV/dxSFAUWiwUAYLfbYbfboSgKvvnmG2RlZQEAJk6c6PB+fPnll8jJyUFYWBgSEhLQq1cvpyNY7itSbGYAAAv1SURBVMaNjIwEUPcattlsDm8rI66maXj55ZfxxBNPOL2tjLjuaO97R3FxMTZs2IApU6YAqHtsZf99HcV1V3vvrzPe+Fxqq/a8d1RUVGDr1q0Nj3NYWBg6deok/bOhsa+//hoJCQno2bOnz+K6oz2PM1A34mmz2WC322Gz2dCtWzevvJaax42NjcWIESOgKAoURcE111zj9udUaziK6472/H2LioqQlpYGs9kMo9GI6667DmvXrpX+vHIW1x2tfV45Oo90dv/qjyuKggEDBuDixYs4ffp0k9uePn0aFRUVGDhwIBRFwYQJE1r1vG4s6AqikpISxMXFNfy/e/fuLV4sJSUl6NGjBwDAaDSiY8eOKC0t9Xgun3zyCYYPH+709/fccw8mTZqEf/zjHx6LuW3bNnTt2hVXXHGFV+I+88wzePnllzFixAi89NJLmD17dos27vxN3HXixAns27cPaWlpOHfuXMObVGxsLM6fPy8tduO47pAd19lzS1bcDz74ALm5uZg7d67D4XoZcb3x3NI0DXl5eRgyZAiGDBmChIQEdOrUqeHkMS4uzmF/no5b/zjPnTsXQ4cOxZEjRzB9+nSvxF20aBFuvvlmlx/4su7va6+9htzcXLz44osOp9u0N+6LL76IJ554Aqpa91FXWlrqlb9v87j1ZN/feq7e42V+Ljm6rcz3juPHj6NLly6YO3cuJkyYgGeffRaVlZVe/WxYsWJFwxeQvooLyH2cu3fvjrvvvhujRo3CsGHDEBkZiZSUFOmvJUdxhw0b1vD72tpa5Ofn46abbnJ4++rqakyaNAm33nqrwyKiLXFlvob79OmDbdu2obS0FFVVVdi4cSOKi4ulP6+cxQW889nv7P4179vRc8ydNu4KuoLI0UhP828j3WnTXm+++SYMBgPGjx/v8PcfffQRlixZgnfffRcffPABtm7d6pG4y5cvdzk65Om4H330EebOnYuCggLMnTsXzz77bIs2nnq8rVYrZs2ahWeeeabh2/TL8URsf4vr6rklI+7tt9+OtWvXIj8/H7Gxsfjf//1fr8T1xnPLYDAgPz8fBQUF2L17N44cOeJWf56Oe/DgQQDA7373O2zatAlJSUn4/PPPpcfdunUrVq1ahWnTprm8nYz7O3v2bKxatQqffPIJLly44HDueXvirl+/Hl26dEFqaqrLdp7++zqLK/v+1nP1Hi/zc8nRbWW/d9jtduzduxe33347li5dCrPZ7Pa1MZ54rGtqarBu3TpkZ2e7fRsZcWU/zhcuXMCXX36JL7/8Eps2bWo4aXanP0/Hzc/Pb/j9888/j/T0dKSnpzu8/fr16/Hpp5/iD3/4A1588UUcO3asXXFlv4aTkpJw77334u6778a9996Lvn37wmAwuHVbGXG99dnvjLfP54OuIIqLi2syHaCkpKTFN59xcXE4deoUgLo31PLyckRFRXkshyVLlmDDhg145ZVXnP5hunfvDgDo2rUrMjIyWjWE7IzdbsfatWsxduxYp208Hbf+Ij4AGDNmjMP+3PmbXE5tbS1mzZqF3Nzchnhdu3ZtGD49ffo0unTp4vHYjuK6Q1bcyz23ZMSNiYmBwWCAqqq45ZZbHC4kISOut55bANCpUyfccMMN2LlzJy5evAi73Q6gbuqTo/48Hbfx1AaDwYCxY8dizZo10uN+++23OHbsGDIzMzF69GhUVVUhIyNDetxNmzYhNjYWiqIgLCwMkyZN8vjzavv27Vi3bh1Gjx6N2bNn45tvvsH//M//SP/7Ooo7Z84c6fe3nrP3eNmfS45uK/u9Iy4uDnFxcQ0jjtnZ2di7d69XPhuAuovJU1JSEBMTA8A7n0mO4sp+nLds2YL4+Hh06dIFHTp0QGZmJnbs2CH9teQsLgD8+c9/xvnz5zF37lynt69/TiYkJOD666/H3r172xXXG6/hW265BUuWLMEHH3yAqKgo9OrVyyvPK0dxvfHZDzh/3TTv29FzzJ027gq6gujqq6/Gjz/+iOPHj6OmpgYrVqzA6NGjm7QZPXp0wwocq1evxuDBgz1W0W7cuBHvvvsu3nzzTZjNZodtKisrUVFR0fDz5s2b/3979xMS1RqHcfwnXIKoNkUSQosc7Z/gspKaFs5o1JzRxEBaJBSFuHBRI0RFYO2KEl3kKloUSGWEIC6aBhvC1JLCCmpZTRC1aGpalBX63EV4yO4M19Izc+fO9wOCvs7hec+Z95wzP87rq5WXl887e2RkxEpLS2c9PvQ6t7i42B48eGBmZmNjY2mn6m3bts2Gh4ctlUpZKpWy4eHhWY+9/40kO3HihJWWltr+/fvd9urqauvv7zczs/7+fgsEAguanSl3LuYyDn83dy5jy4vcn+fsxmKxtGPGi1yvx1YymXRX25qcnLSRkRHz+Xy2efNmu3Xrlpn9+BCZbj+qq6ttcHDQvn37Zq9fv7aXL19aZWXlH+euWbPGXr165R6PO3fupP07tYXOraiosHv37tnQ0JANDQ3Z4sWL084dX+jc0tJSd1xJyjiu5vP+RiIRu3v3rg0NDVlnZ6dt2bLFzp8/7/n7my733Llznu+vWeZrvNf3pUzben3tWLlypa1atcp9sjs6Omo+n8/ze8OMwcFBC4VC7s+5yvX6OJeUlNjjx4/ty5cvJslGR0etrKzM83MpXa7P57O+vj4bHh62zs7Of0xLnZFKpdwpbclk0h49emRlZWXzys3GOfz+/XszM3vz5o1Fo1FzHCcr4ypdrtfjakam/Ztpl2QTExO2bNmyfxQ7xcXFtmTJEpuYmDBJGY/PnPzRUgz/cfF4XLW1tQoEAurp6ZEkdXV1KRaLSZImJyfV1tamYDCoxsZGJRKJP8o5fPiwtm7dqo0bN8rv9+v69esKBoPavn276urqVFdX565m9/btWx08eFCSlEgkFA6HFQ6HtWvXLreP88mVfqxi1NvbO+u1XueOj4+roaFB4XBYe/bs0dOnTyX9WLXq+PHj7rZ9fX0KBoMKBoO6cePGb+WOj49r7dq1chzHPa7xeFzJZFLNzc2qqalRc3OzPnz4sKDZmXKj0aj8fr8qKipUVVWlAwcOSJp9rKX043A+uXMZW17ktre3y3EcOY6jlpYWvXv3Liu5Xo+t58+fq76+Xo7jKBQKuSszJhIJNTY2KhgMqq2tTV+/fpUkxWIxdXV1udv39PQoEAiotrb2t1a1SZc7NTWlpqYmt+3IkSPuqnNe5v7q51WZvM7dt2+f2xaJRNyV6Bby2jFjbGzMXe3N6/c3U2429jfTNd7r+1Kmbb2+dkjSs2fP1NDQIMdx1Nraqo8fP3p+b5Ckz58/a9OmTfr06ZPblqvcbBzn7u5u7dixQ6FQSO3t7e6qcV6fS+lyN2zYoEAg4I7nmWvKz8f54cOHchxH4XBYjuO4n5Pmk5uNc3jv3r3auXOnwuGwRkZGJGVnXKXL9WJcpfscmWn/pqen1dHRoUAgIMdxZq3qW1dX537/5MkThUIhBQIBnTp1StPT07+17zOKpD9dsBsAAAAA8tv/bsocAAAAAMwVBREAAACAgkVBBAAAAKBgURABAAAAKFgURAAAAAAKFgURACAv3L592+rr62d9rV+/3np7e23dunV25coV97WnT5+2mzdv5rC3AIB88VeuOwAAwFzU1NRYTU2N+/O1a9dsYGDA/H6/rVixwi5fvmxNTU22aNGiHPYSAJBveEIEAMg7L168sAsXLtjZs2etqKjIli9fblVVVe5/PAcAYK4oiAAAeeX79+8WiUTs6NGjVlJS4rYfOnTILl26ZFNTUznsHQAg31AQAQDySnd3t5WXl1soFJrVvnr1aqusrLSBgYEc9QwAkI8oiAAAeeP+/fsWjUbt5MmTaX/f0tJiFy9etOnp6Sz3DACQryiIAAB5IZVK2bFjx+zMmTO2dOnStK/x+Xzm8/ksHo9nt3MAgLzFKnMAgLxw9epVSyaT1tHRMav916lzra2ttnv37iz2DACQz4okKdedAAAAAIBcYMocAAAAgIJFQQQAAACgYFEQAQAAAChYFEQAAAAAChYFEQAAAICCRUEEAAAAoGBREAEAAAAoWBREAAAAAArW3//tkSdkJ2FcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "sns.boxenplot(x='ZN',y='MEDV',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看CHAS与label的相关性小于0.1，画出提琴图.观察到，CHAS的取值对MEDV有一些影响，故也不作处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61361d4c88>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='CHAS',y='MEDV',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利用独热编码处理离散特征变量\n",
    "之前已经知道CHARS,RAD为类别特征，这里将其改成独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHAS_0.0</th>\n",
       "      <th>CHAS_1.0</th>\n",
       "      <th>RAD_1.0</th>\n",
       "      <th>RAD_2.0</th>\n",
       "      <th>RAD_3.0</th>\n",
       "      <th>RAD_4.0</th>\n",
       "      <th>RAD_5.0</th>\n",
       "      <th>RAD_6.0</th>\n",
       "      <th>RAD_7.0</th>\n",
       "      <th>RAD_8.0</th>\n",
       "      <th>RAD_24.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHAS_0.0  CHAS_1.0  RAD_1.0  RAD_2.0  RAD_3.0  RAD_4.0  RAD_5.0  RAD_6.0  \\\n",
       "0         1         0        1        0        0        0        0        0   \n",
       "1         1         0        0        1        0        0        0        0   \n",
       "2         1         0        0        1        0        0        0        0   \n",
       "3         1         0        0        0        1        0        0        0   \n",
       "4         1         0        0        0        1        0        0        0   \n",
       "\n",
       "   RAD_7.0  RAD_8.0  RAD_24.0  \n",
       "0        0        0         0  \n",
       "1        0        0         0  \n",
       "2        0        0         0  \n",
       "3        0        0         0  \n",
       "4        0        0         0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_classic = pd.get_dummies(X_classic,columns=X_classic.columns)\n",
    "X_classic.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理好的数据保存到文件\n",
    "##### 综上，对数据作了如下处理：\n",
    "- 对label进行去噪\n",
    "- 对label进行log1p处理\n",
    "- 对离散数据进行独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./bosten_Data_handle.csv','w') as f:\n",
    "    pd.concat([X_values,X_classic,y],axis=1,sort=False).to_csv(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTAATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RAD_1.0</th>\n",
       "      <th>RAD_2.0</th>\n",
       "      <th>RAD_3.0</th>\n",
       "      <th>RAD_4.0</th>\n",
       "      <th>RAD_5.0</th>\n",
       "      <th>RAD_6.0</th>\n",
       "      <th>RAD_7.0</th>\n",
       "      <th>RAD_8.0</th>\n",
       "      <th>RAD_24.0</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.218876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.117950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.575151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.538057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.616309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     CRIM    ZN  INDUS    NOX     RM   AGE     DIS    TAX  \\\n",
       "0           0  0.00632  18.0   2.31  0.538  6.575  65.2  4.0900  296.0   \n",
       "1           1  0.02731   0.0   7.07  0.469  6.421  78.9  4.9671  242.0   \n",
       "2           2  0.02729   0.0   7.07  0.469  7.185  61.1  4.9671  242.0   \n",
       "3           3  0.03237   0.0   2.18  0.458  6.998  45.8  6.0622  222.0   \n",
       "4           4  0.06905   0.0   2.18  0.458  7.147  54.2  6.0622  222.0   \n",
       "\n",
       "   PTAATIO  ...  RAD_1.0  RAD_2.0  RAD_3.0  RAD_4.0  RAD_5.0  RAD_6.0  \\\n",
       "0     15.3  ...        1        0        0        0        0        0   \n",
       "1     17.8  ...        0        1        0        0        0        0   \n",
       "2     17.8  ...        0        1        0        0        0        0   \n",
       "3     18.7  ...        0        0        1        0        0        0   \n",
       "4     18.7  ...        0        0        1        0        0        0   \n",
       "\n",
       "   RAD_7.0  RAD_8.0  RAD_24.0      MEDV  \n",
       "0        0        0         0  3.218876  \n",
       "1        0        0         0  3.117950  \n",
       "2        0        0         0  3.575151  \n",
       "3        0        0         0  3.538057  \n",
       "4        0        0         0  3.616309  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./bosten_Data_handle.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分别对特征进行标准化和归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.70897099 -0.41740129  0.30181252 -1.29185589 -0.13989479  0.50503976\n",
      "  -0.10943189  0.12120789 -0.66710128 -1.52727975  0.44353452 -1.12267925\n",
      "   0.25081213 -0.25081213  4.97890286 -0.22694084 -0.28579311 -0.53171657\n",
      "  -0.53487311 -0.23671604 -0.1895806  -0.22192462 -0.59149155]\n",
      " [-1.70219846 -0.41499231 -0.4869242  -0.59332877 -0.73182065  0.26901737\n",
      "   0.37748816  0.53733022 -0.98873354 -0.34150355  0.44353452 -0.53477219\n",
      "   0.25081213 -0.25081213 -0.20084746  4.40643469 -0.28579311 -0.53171657\n",
      "  -0.53487311 -0.23671604 -0.1895806  -0.22192462 -0.59149155]\n",
      " [-1.69542594 -0.41499461 -0.4869242  -0.59332877 -0.73182065  1.43993368\n",
      "  -0.25515249  0.53733022 -0.98873354 -0.34150355  0.39955339 -1.25693687\n",
      "   0.25081213 -0.25081213 -0.20084746  4.40643469 -0.28579311 -0.53171657\n",
      "  -0.53487311 -0.23671604 -0.1895806  -0.22192462 -0.59149155]\n",
      " [-1.68865341 -0.41441159 -0.4869242  -1.31093331 -0.82618565  1.15333505\n",
      "  -0.79893911  1.05687823 -1.1078566   0.08537589  0.4190045  -1.41097983\n",
      "   0.25081213 -0.25081213 -0.20084746 -0.22694084  3.49903462 -0.53171657\n",
      "  -0.53487311 -0.23671604 -0.1895806  -0.22192462 -0.59149155]\n",
      " [-1.68188088 -0.41020191 -0.4869242  -1.31093331 -0.82618565  1.38169439\n",
      "  -0.50038959  1.05687823 -1.1078566   0.08537589  0.44353452 -1.07321592\n",
      "   0.25081213 -0.25081213 -0.20084746 -0.22694084  3.49903462 -0.53171657\n",
      "  -0.53487311 -0.23671604 -0.1895806  -0.22192462 -0.59149155]]\n",
      "[[0.00000000e+00 0.00000000e+00 1.80000000e-01 5.81481481e-02\n",
      "  3.14814815e-01 5.77505269e-01 6.41606591e-01 2.68711042e-01\n",
      "  2.08015267e-01 2.87234043e-01 1.00000000e+00 8.33564879e-02\n",
      "  1.00000000e+00 0.00000000e+00 1.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
      " [1.98019802e-03 2.35922539e-04 0.00000000e+00 2.34444444e-01\n",
      "  1.72839506e-01 5.47997701e-01 7.82698249e-01 3.48523591e-01\n",
      "  1.04961832e-01 5.53191489e-01 1.00000000e+00 1.98944151e-01\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
      " [3.96039604e-03 2.35697744e-04 0.00000000e+00 2.34444444e-01\n",
      "  1.72839506e-01 6.94385898e-01 5.99382080e-01 3.48523591e-01\n",
      "  1.04961832e-01 5.53191489e-01 9.89737254e-01 5.69602667e-02\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
      " [5.94059406e-03 2.92795719e-04 0.00000000e+00 5.33333333e-02\n",
      "  1.50205761e-01 6.58555279e-01 4.41812564e-01 4.48173256e-01\n",
      "  6.67938931e-02 6.48936170e-01 9.94276060e-01 2.66740761e-02\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
      " [7.92079208e-03 7.05070075e-04 0.00000000e+00 5.33333333e-02\n",
      "  1.50205761e-01 6.87104809e-01 5.28321318e-01 4.48173256e-01\n",
      "  6.67938931e-02 6.48936170e-01 1.00000000e+00 9.30814115e-02\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wangxi/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/wangxi/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "/home/wangxi/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "X_standard = StandardScaler().fit_transform(data.drop('MEDV',axis=1))\n",
    "X_minmax = minmax_scale(data.drop('MEDV',axis=1))\n",
    "y = data['MEDV']\n",
    "print(X_standard[:5,:])\n",
    "print(X_minmax[:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  分割训练数据和测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_standard_train,X_standard_test,y_standard_train,y_standard_test = train_test_split(X_standard,y,test_size=0.1,random_state=33)\n",
    "X_minmax_train,X_minmax_test,y_minmax_train,y_minmax_test = train_test_split(X_minmax,y,test_size=0.1,random_state=33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入模型训练所需的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最小二乘线性模型训练，并得分\n",
    "结果几乎没有差别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标准化参数使用最小二乘的预测得分： 0.8038905326820452\n",
      "归一化参数使用最小二乘预测得分： 0.8038905326820451\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_standard_train,y_standard_train)\n",
    "score = lr.score(X_standard_test,y_standard_test)\n",
    "print('标准化参数使用最小二乘的预测得分：',score)\n",
    "\n",
    "lr.fit(X_minmax_train,y_minmax_train)\n",
    "score = lr.score(X_minmax_test,y_minmax_test)\n",
    "print('归一化参数使用最小二乘预测得分：',score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用超参数调优岭回归模型训练，并得分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "超参调优岭回归，标准化特征预测得分： 0.8047291794794804\n",
      "超参调优岭回归，归一化特征预测得分： 0.8102674052858784\n"
     ]
    }
   ],
   "source": [
    "rid = Ridge()\n",
    "clf = GridSearchCV(rid,{'alpha':np.arange(0.01,1,0.05)},refit=True,cv=3)\n",
    "clf.fit(X_standard_train,y_standard_train)\n",
    "score = clf.score(X_standard_test,y_standard_test)\n",
    "print('超参调优岭回归，标准化特征预测得分：',score)\n",
    "\n",
    "clf.fit(X_minmax_train,y_minmax_train)\n",
    "score = clf.score(X_minmax_test,y_minmax_test)\n",
    "print('超参调优岭回归，归一化特征预测得分：',score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用超参数调优Lasso回归模型训练，并得分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "超参调优Lasso回归，标准化特征预测得分： 0.7904657920378676\n",
      "超参调优Lasso回归，归一化特征预测得分： 0.724122572689812\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "clf = GridSearchCV(lasso,{'alpha':np.arange(0.01,1,0.05)},refit=True,cv=3)\n",
    "clf.fit(X_standard_train,y_standard_train)\n",
    "score = clf.score(X_standard_test,y_standard_test)\n",
    "print('超参调优Lasso回归，标准化特征预测得分：',score)\n",
    "\n",
    "clf.fit(X_minmax_train,y_minmax_train)\n",
    "score = clf.score(X_minmax_test,y_minmax_test)\n",
    "print('超参调优Lasso回归，归一化特征预测得分：',score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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